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SPS-100 | IBMSPSSSTATL1P - IBM SPSS Statistics Level 1

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SPS-100 - IBMSPSSSTATL1P - IBM SPSS Statistics Level 1 - braindump

Vendor IBM
Exam Number SPS-100
Exam Name IBMSPSSSTATL1P - IBM SPSS Statistics Level 1
Questions 70 Q & A
Recent Update February 18, 2019
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SPS-100 exam Dumps Source : IBMSPSSSTATL1P - IBM SPSS Statistics Level 1

Test Code : SPS-100
Test Name : IBMSPSSSTATL1P - IBM SPSS Statistics Level 1
Vendor Name : IBM
Q&A : 70 Real Questions

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IBM IBMSPSSSTATL1P - IBM SPSS

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complete comparison of Axial size dimension With Three Swept-source OCT-based Biometers and Partial Coherence Interferometry | killexams.com Real Questions and Pass4sure dumps

the first commercially attainable optical biometer become the IOLMaster (Carl Zeiss Meditec AG, Jena, Germany), which was delivered in 1999. It became greatly adopted as the gold common for intraocular lens alternative on the time of cataract surgery.1 it's in line with partial coherence interferometry (PCI). decent precision of the gadget has been pronounced, however the failure expense for axial size size is about 17.5% to 37.eighty four%.1–5 Optical low coherence reflectometry and optical low coherence interferometry have become accessible in more fresh years, and maybe function greater in terms of acquisition of axial length measurements in denser cataracts than the PCI-based mostly IOLMaster.

however optical biometers deliver additional measurements akin to keratometry and supply higher resolution than ultrasound, their software is restricted because of the penetration of easy. better failure prices occur with corneal scars, dense cataracts, and vitreous hemorrhage. Some new algorithms have been utilized to increase the signal-to-noise ratio, akin to a series of consecutive measurements.6 Swept-source optical coherence tomography (SS-OCT) is a comparatively new technique of Fourier area OCT enabling stronger penetration. Three contraptions have turn into purchasable using this expertise: the IOL-master seven hundred (Carl Zeiss Meditec AG), the OA-2000 (Tomey, Nagoya, Japan), and the Argos (Movu Inc., Komaki, Japan).7–10 To our advantage, there was no assessment of those SS-OCT biometers or an evaluation of their repeatability and contract.

The purpose of the existing look at turned into to compare the IOLMaster seven-hundred, the OA-2000, and the Argos to a PCI-based mostly biometer (the IOLMaster v5.4) for axial size size failure fees in sufferers with cataract. furthermore, the present examine sought to prospectively examine the repeatability of the three SS-OCT biometers in evaluation to the PCI-based mostly IOLMaster v5.4 and the settlement between all gadgets.

sufferers and MethodsPatients

This prospective look at enrolled 119 patients (171 eyes) with cataract who were as a result of undergo cataract surgery on the Eye medical institution of Wenzhou scientific institution, Wenzhou, China. The purpose of the examine become totally defined and all participants signed written advised consent. The look at become authorised with the aid of the Board of Wenzhou scientific school in keeping with the tenets of the announcement of Helsinki. All contributors underwent complete ophthalmic examinations that mainly consisted of subjective refraction, noncontact tonometry, slit-lamp biomicroscopy, and ophthalmoscopy. The inclusion criterion turned into adult sufferers with unilateral or bilateral cataract. The exclusion criteria had been: fresh heritage of ocular trauma, extreme corneal or vitreous opacities, significant retinal disease, severe systemic ailment, nystagmus, or inadequate potential to agree to testing.

Following student dilation with 0.5% tropicamide and zero.5% phenylephrine hydrochloride (Mydrin-P; Santen Pharmaceutical, Osaka, Japan), each affected person turned into examined on a slit-lamp biomicroscope and the severity of cataract graded in response to the Lens Opacities Classification device (LOCS) III.eleven The LOCS III incorporates different slit-lamp photographs for grading nuclear colour, nuclear opalescence, cortical cataract, and posterior subcapsular cataract on a decimal scale. the size levels from 0.1 to 5.9 for grading cortical cataract or posterior subcapsular cataract and from 0.1 to 6.9 for nuclear color or nuclear opalescence. greater scores point out stronger severity.

gadgets and methods

Axial size values had been measured following pupil dilation via the 4 different biometers through the same experienced operator (YL). The order through which the biometers were used became randomized. All measurements had been taken within the shortest time length viable and measurements were captured between the times 10:00 and 17:00.

The OA-2000 is a biometer in line with Fourier area expertise via light interference the usage of a swept-supply laser with a wavelength of 1,060 nm.12,13 The OA-2000 performs B-scans for measuring the ocular parameters to discover the clearest a part of the lens automatically. When blinks are detected, the OA-2000 eliminates the unsuitable records and retakes the photograph immediately. The IOLMaster seven-hundred accommodates SS-OCT and a multi-dot keratometer.14 The laser used is a vertical cavity floor-emitting laser with an extended coherence wavelength of 1,055 nm. The machine offers a 1-mm horizontal OCT scan of the retina to ensure that the measurements are on the visible axis by using the presence of the foveal pit. The Argos SS-OCT makes use of a 1,060-nm wavelength and 20-nm bandwidth swept-supply know-how to assemble a two-dimensional OCT scan of the eye.7 The Argos displays whole eye true time OCT all over alignment to be certain a hit measurement. The IOLMaster v5.four uses PCI to measure axial length.1,15 It uses a laser diode emitting a 780-nm infrared beam of brief-coherent light.

The composite-20 formulation turned into used within the latest look at, which has been shown to deliver more constant success for axial size measurement than the typical or typical composite-5 method. With the OA-2000, IOLMaster seven-hundred, and Argos, at the least three official measurements have been recorded in keeping with the manufacturer's guidelines.

Statistical evaluation

Statistical analysis became carried out the usage of SPSS for windows utility (version 21.0; IBM enterprise, Armonk, big apple) and MedCalc Statistical utility (edition 13.0; MedCalc utility, Inc., Mariakerke, Belgium). The Kolmogorov–Smirnov verify became used to determine that the information had been normally dispensed (P > .05).

The consequences had been calculated for three measurements and presented as the imply ± standard deviation. Logistic regression evaluation changed into used to verify the association between cataract class and severity with acquisition expense. Chi-square analysis changed into used to verify even if statistically massive adjustments in success rates had been found between the biometers. within-subject typical deviation (Sw), look at various–retest repeatability (TRT), coefficient of adaptation (CoV), and intraclass correlation coefficients (ICCs) were calculated for the three measurements to verify the intraobserver repeatability.sixteen,17 The TRT described as 2.seventy seven Sw suggests the interval inside which ninety five% of the modifications between measurements are expected to lie.18 The CoV turned into calculated as the ratio of the Sw to the suggest (the reliability turned into enhanced with lessen values). The ICC ranging from 0 to 1 is often categorised as follows: an ICC below 0.seventy five indicates a negative correlation, whereas an ICC enhanced than 0.ninety indicates a high correlation.19,20 For agreement assessment, paired t exams and Bland–Altman plots were carried out and ninety five% limits of agreement (LoA) were calculated.21 Narrower 95% LoA point out higher agreement. P values below .05 had been considered statistically colossal.

outcomes

one hundred nineteen patients had been enrolled and 171 eyes have been measured in total. The suggest age of patients turned into sixty eight.87 ± 9.82 years (range: 38 to 88 years). From the 171 eyes, the measurement success rate become 166 eyes (97.08%) with the IOLMaster seven hundred, 166 eyes (97.08%) with the OA-2000, 170 eyes (99.42%) with the Argos, and 138 eyes (eighty.70%) with the IOLMaster v5.four. The consequences of LOCS III classification are proven in table A (available within the online version of this article). Logistic regression evaluation become applied for the association between cataract category and severity with acquisition expense (desk B, purchasable within the online edition of this text). The evaluation showed no affiliation with cortical, nuclear, or posterior subcapsular cataract with the acquisition fee of axial length measurements with most biometers, but a statistically colossal association for posterior subcapsular cataract with the IOLMaster 700 (β = −1.one hundred fifteen, P = .042) and the IOLMaster v5.4 (β = −2.139, P < .001). improved severity of posterior subcapsular cataract changed into linked to a more robust axial size measurement failure fee. McNemar's exams were used to determine even if statistically large modifications in success quotes were found between the 4 biometers with chi-rectangular analysis (desk C, attainable in the online edition of this text). The results indicated giant ameliorations in axial size measurement success prices between the IOLMaster 700 vs IOLMaster v5.four, OA-2000 vs IOLMaster v5.four, and Argos vs IOLMaster v5.4 (P < .001). No statistically enormous modifications had been discovered between the three SS-OCT biometers.

Type and Severity of Cataracts Based on Lens Opacities Classification System (LOCS) III

table A:

category and Severity of Cataracts in line with Lens Opacities Classification equipment (LOCS) III

Logistic Regression Analysis Assessing the Association Between Cataract Type and Severity With Acquisition Rate

desk B:

Logistic Regression evaluation Assessing the affiliation Between Cataract class and Severity With Acquisition cost

Differences in Success Rates Between the SS-OCT Biometersa

table C:

changes in Success rates Between the SS-OCT Biometers

The intraobserver repeatability consequences of all patients effectively measured with the IOLMaster seven hundred, OA-2000, and Argos are displayed in desk 1. all the SS-OCT biometers confirmed excessive repeatability with low TRT (0.03, 0.06, and 0.05 mm, respectively), low CoV (0.04%, 0.10%, and 0.07%, respectively), and excessive ICC values (1.000, 0.999, and 1.000, respectively).

Intraobserver Repeatability Outcomes for Axial Length Measurements

table 1:

Intraobserver Repeatability results for Axial size Measurements

except for the failed measurements, 138 eyes have been effectively measured with the aid of all 4 biometers and the effects are proven in table 2. The IOLMaster seven hundred, OA-2000, and Argos confirmed equivalent miraculous repeatability. the bland–Altman evaluation and LoA (Figures A–D, purchasable within the online edition of this text) indicated astounding settlement among the three SS-OCT–primarily based biometers and the IOLMaster v5.four, with ninety five% LoA as slender as 0.09 mm (desk 3).

Intraobserver Repeatability Outcomes for Common Axial Length Measurements (N = 138)

desk 2:

Intraobserver Repeatability outcomes for usual Axial length Measurements (N = 138)

Bland–Altman plots of agreement in axial length measurement between the IOLMaster 700 and OA-2000. The solid line indicates the mean difference (bias). The upper and lower lines represent the 95% limits of agreement (Figure A shows the mean results of all patients successfully measured, Figure B shows the mean results of patients successfully measured with all four biometers). The IOLMaster is manufactured by Carl Zeiss Meditec AG, Jena, Germany; and the OA-2000 is manufactured by Tomey, Nagoya, Japan. SD = standard deviation

figure A.

Bland–Altman plots of agreement in axial length dimension between the IOLMaster seven hundred and OA-2000. The solid line suggests the imply change (bias). The upper and decrease strains symbolize the 95% limits of contract (figure A indicates the imply results of all sufferers correctly measured, determine B suggests the suggest effects of patients successfully measured with all four biometers). The IOLMaster is manufactured by means of Carl Zeiss Meditec AG, Jena, Germany; and the OA-2000 is manufactured through Tomey, Nagoya, Japan. SD = typical deviation

Bland–Altman plots of agreement in axial length measurement between the IOLMaster 700 and Argos. The solid line indicates the mean difference (bias). The upper and lower lines represent the 95% limits of agreement (Figure A shows the mean results of all patients successfully measured, Figure B shows the mean results of patients successfully measured with all four biometers). The IOLMaster is manufactured by Carl Zeiss Meditec AG, Jena, Germany; and the Argos is manufactured by Movu Inc., Komaki, Japan. SD = standard deviation

determine B.

Bland–Altman plots of settlement in axial length measurement between the IOLMaster seven-hundred and Argos. The strong line shows the suggest change (bias). The higher and decrease strains signify the ninety five% limits of contract (determine A shows the mean results of all patients successfully measured, figure B suggests the suggest outcomes of patients correctly measured with all four biometers). The IOLMaster is manufactured by means of Carl Zeiss Meditec AG, Jena, Germany; and the Argos is manufactured by way of Movu Inc., Komaki, Japan. SD = commonplace deviation

Bland–Altman plots of agreement in axial length measurement between the OA-2000 and Argos. The solid line indicates the mean difference (bias). The upper and lower lines represent the 95% limits of agreement (Figure A shows the mean results of all patients successfully measured, Figure B shows the mean results of patients successfully measured with all four biometers). The OA-2000 is manufactured by Tomey, Nagoya, Japan; and the Argos is manufactured by Movu Inc., Komaki, Japan. SD = standard deviation

figure C.

Bland–Altman plots of settlement in axial length size between the OA-2000 and Argos. The solid line suggests the imply difference (bias). The higher and reduce strains characterize the ninety five% limits of contract (figure A indicates the mean effects of all patients efficaciously measured, figure B suggests the suggest effects of sufferers correctly measured with all four biometers). The OA-2000 is manufactured by using Tomey, Nagoya, Japan; and the Argos is manufactured by Movu Inc., Komaki, Japan. SD = ordinary deviation

Bland–Altman plots of agreement in axial length measurement among the IOLMaster 700, OA-2000, Argos, and IOLMaster v5.4. The solid line indicates the mean difference (bias). The upper and lower lines represent the 95% limits of agreement (Figure A shows the agreement between the IOLMaster 700 and IOLMaster v5.4, Figure B shows the agreement between the OA-2000 and IOLMaster v5.4, and Figure C shows the agreement between the Argos and IOLMaster v5.4). The IOLMaster is manufactured by Carl Zeiss Meditec AG, Jena, Germany; the OA-2000 is manufactured by Tomey, Nagoya, Japan; and the Argos is manufactured by Movu Inc., Komaki, Japan. SD = standard deviation

determine D.

Bland–Altman plots of settlement in axial size size among the IOLMaster seven-hundred, OA-2000, Argos, and IOLMaster v5.four. The solid line shows the mean change (bias). The higher and decrease strains characterize the 95% limits of agreement (determine A suggests the settlement between the IOLMaster seven-hundred and IOLMaster v5.4, figure B shows the settlement between the OA-2000 and IOLMaster v5.4, and determine C shows the settlement between the Argos and IOLMaster v5.4). The IOLMaster is manufactured by way of Carl Zeiss Meditec AG, Jena, Germany; the OA-2000 is manufactured with the aid of Tomey, Nagoya, Japan; and the Argos is manufactured by way of Movu Inc., Komaki, Japan. SD = ordinary deviation

Axial Length Differences Between Devices

desk 3:

Axial length ameliorations Between contraptions

dialogue

Axial size is an quintessential dimension for cataract surgical procedure, with 54% of the refractive error in intraocular lens power calculation as a result of axial size.22,23 A 1-mm axial size error can lead to a refractive error of about 2.70 to three.00 diopters (D).6,24 The knowledge of optical biometers is their excessive resolution in evaluation to ultrasound, but they depend on the media transparency.1,25

within the present look at, the failure price of measurement of axial size with the PCI-based mostly IOLMaster v5.4 became 19.30%. this is comparable to outdated reviews showing a failure rate of 17.5% to 37.84%.2–four The SS-OCT–based IOLMaster seven hundred, OA-2000, and Argos showed lots reduce failure quotes of 2.92%, 2.92%, and 0.fifty eight%, respectively. This difference between the SS-OCT–based mostly biometers and the PCI-primarily based IOLMaster v5.four became statistically colossal (all P < .001 with chi-square evaluation), however there were no statistically massive alterations among the three SS-OCT biometers, indicating the performance and penetration turned into similar. Akman et al.14 stated the success price of axial length dimension with the IOLMaster seven hundred changed into 100% and the PCI-primarily based IOLMaster-500 failed in 17 instances from 188 eyes. In a previous analyze of ours,three the success price of the OA-2000 and IOLMaster v5.4 was 100% and sixty three.ninety three%, respectively. The IOLMaster v5.4 is in response to the PCI precept with a wavelength of 780 nm, whereas the IOLMaster seven hundred uses a swept-supply easy with a wavelength varying from 1,035 to 1,095 nm. A swept-supply light of 1,060 nm is used within the OA-2000 and the Argos. therefore, we accept as true with an extended wavelength of light and improvement of approach might also aid to enhance the success fee for axial size measurements.

The diploma of lens opacification in a cataractous lens is not all the time uniform. according to the logistic regression analysis of the current look at, cortical and nuclear lens opacities had no tremendous influence on the failure of axial length measurements with any of the 4 biometers. despite the fact, posterior subcapsular opacity was the leading culprit inflicting acquisition failure with the PCI-primarily based IOLMaster v5.four. Posterior subcapsular opacities continually trigger more visual impairment and affect distinction sensitivity even at an early stage.26–28 The reason is likely as a result of the incontrovertible fact that posterior subcapsular opacities are nearer to the nodal aspect and with better density, which may impact the propagation of easy. In outdated reports evaluating the PCI-based mostly IOLMaster with different biometers, a statistically tremendous vogue of better failure rates with expanding severity of posterior subcapsular cataracts has been discovered.2,three,29

To reap authentic axial length values, fixation is crucial all the way through measurement. When measuring with ultrasound, it is difficult to attain measurements with recognize to the fovea. The PCI-based mostly IOLMaster v5.four and the SS-OCT–based mostly OA-2000 give a fixation mild for the patient, however will also be tricky to profit repeatable measurements for those with bad visible acuity. In a old analyze comparing the PCI-primarily based IOLMaster and the SS-OCT–primarily based IOLMaster seven hundred, 19.6% nevertheless showed fixation loss.9 The IOLMaster seven hundred and the Argos supply an additional image of the complete eye in a longitudinal part, allowing the observer to make sure that the anteroposterior axis falls at once on the fovea.

The outcomes of the three SS-OCT biometers showed excessive repeatability with low Sw, low TRT, low CoV, and high ICC. With reanalysis of the 138 eyes measured with the aid of all 4 biometers, the repeatability turned into encouraging. The repeatability of the IOLMaster seven-hundred within the present study became comparable to that in the look at through Kunert et al.,30 who stated a TRT of 0.025 mm and a CoV of 0.03%. As in the present study, the OA-2000 has validated excessive repeatability in a number of articles with the TRT varying from 0.04 to 0.07 mm, CoV values various from 0.06 to 0.10, and ICC values close to 1.000.12,13 The excellent repeatability of those SS-OCT–based biometers is encouraging.

As part of the assessment of any new device, it is crucial to examine settlement among an identical contraptions.31,32 as the results exhibit in table 6 and in the figures, the imply change among the 4 biometers changed into near 0 mm with out statistical importance. Yang et al.9 discovered that in patients with myopia and an axial size of 27 mm, the suggest difference between IOLMaster 700 and IOLMaster v5.4 became greater, specially for these with posterior staphyloma (imply change of 0.095 ± 0.182 mm, range: −0.eleven to 1.forty eight mm, P < .001). In our previous study evaluating the OA-2000 and the IOLMaster v5.four, the ninety five% LoA latitude become −0.05 to 0.07 mm, which is comparable to the present study.13 The 95% LoA latitude between the Argos and IOLMaster v5.four in the current study changed into a bit narrower than that pronounced through Shammas et al.7 The stages of 95% LoA for the three SS-OCTs had been no more than 0.09 mm, indicating their brilliant settlement. although, the IOLMaster 700 and the Argos have an further characteristic of offering SS-OCT imaging all through size. whether it helps to improve the accuracy in specific patients corresponding to these with excessive myopia requires additional evaluation.

These SS-OCT–based biometers have confirmed superiority in terms of the acquisition expense of axial size measurements in assessment to the PCI-primarily based IOLMaster v5.4. moreover, their measurements are also in agreement, indicating they can be used interchangeably. This has the most important abilities of cutting back the need for much less accurate ultrasound-based mostly axial length measurements. These findings imply a paradigm shift in optical biometry in that SS-OCT–primarily based biometers are prone to become the gold common.

References
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  • table 1

    Intraobserver Repeatability effects for Axial size Measurements

    DeviceEyes (n)suggest ± SD (mm)SwTRTCOV (%)ICC (ninety five% CI)IOLMaster 70016623.24 ± 1.020.010.030.041.000 (1.000 to 1.000)OA-200016623.22 ± 1.010.020.060.100.999 (0.999 to 1.000)Argos17023.22 ± 0.990.020.050.071.000 (1.000 to 1.000)desk 2

    Intraobserver Repeatability consequences for normal Axial size Measurements (N = 138)

    DeviceMean ± SD (mm)SwTRTCOV (%)ICC (ninety five% CI)IOLMaster 70023.26 ± 1.070.010.030.041.000 (1.000 to 1.000)OA-200023.25 ± 1.060.020.060.091.000 (0.999 to 1.000)Argos23.26 ± 1.040.020.050.071.000 (1.000 to 1.000)desk 3

    Axial length differences Between contraptions

    device PairingsMean ± SD (mm)P95% LoAIOLMaster seven hundred vs OA-2000.01 ± 0.03.048−0.05 to 0.06IOLMaster seven-hundred vs Argos0.00 ± 0.041.000−0.08 to 0.09OA-2000 vs Argos0.00 ± 0.041.000−0.09 to 0.08IOLMaster seven hundred vs IOLMaster v5.40.00 ± 0.031.000−0.05 to 0.06OA-2000 vs IOLMaster v5.40.00 ± 0.04.649−0.07 to 0.06Argos vs IOLMaster v5.forty.00 ± 0.041.000−0.09 to 0.09Table A

    class and Severity of Cataracts in keeping with Lens Opacities Classification gadget (LOCS) III

    ScoreCorticalNuclear ColorPosterior Subcapsular⩾ 2.584271042.5 to 3.54483413.6 to four.5304884.6 to 5.510108> 5.5315Table B

    Logistic Regression evaluation Assessing the affiliation Between Cataract class and Severity With Acquisition expense

    DeviceCorticalNuclear ColorPosterior SubcapsularIOLMaster 700  β−2.905−0.076−1.115   P.138.915.042OA-2000  β−1.773−0.617−0.622   P.294.395.185Argos  β−33.53919.375−fifty nine.163   P.992.992.977IOLMaster v5.4  β−1.842−0.497−2.139   P.379.089< .001Table C

    changes in Success quotes Between the SS-OCT Biometersa

    DeviceOA-2000ArgosIOLMaster v5.4IOLMaster 7001.000.a hundred twenty five< .001OA-2000–.125< .001Argos––< .001

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    IBMSPSSSTATL1P - IBM SPSS Statistics Level 1

    Pass 4 sure SPS-100 dumps | Killexams.com SPS-100 real questions | [HOSTED-SITE]

    Comparison of adverse events associated with different spacers used with non-extrafine beclometasone dipropionate for asthma | killexams.com real questions and Pass4sure dumps

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  • Unfriendly Skies: Predicting Flight Cancellations Using Weather Data, Part 2 | killexams.com real questions and Pass4sure dumps

    Ricardo Balduino and Tim Bohn

    Early Flight, Creative Commons Introduction

    As we described in Part 1 of this series, our objective is to help predict the probability of the cancellation of a flight between two of the ten U.S. airports most affected by weather conditions. We use historical flights data and historical weather data to make predictions for upcoming flights.

    Over the course of this four-part series, we use different platforms to help us with those predictions. Here in Part 2, we use the IBM SPSS Modeler and APIs from The Weather Company.

    Tools used in this use case solution

    IBM SPSS Modeler is designed to help discover patterns and trends in structured and unstructured data with an intuitive visual interface supported by advanced analytics. It provides a range of advanced algorithms and analysis techniques, including text analytics, entity analytics, decision management and optimization to deliver insights in near real-time. For this use case, we used SPSS Modeler 18.1 to create a visual representation of the solution, or in SPSS terms, a stream. That’s right — not one line of code was written in the making of this blog.

    We also used The Weather Company APIs to retrieve historical weather data for the ten airports over the year 2016. IBM SPSS Modeler supports calling the weather APIs from within a stream. That is accomplished by adding extensions to SPSS, available in the IBM SPSS Predictive Analytics resources page, a.k.a. Extensions Hub.

    A proposed solution

    In this blog, we propose one possible solution for this problem. It’s not meant to be the only or the best possible solution, or a production-level solution for that matter, but the discussion presented here covers the typical iterative process (described in the sections below) that helps us accumulate insights and refine the predictive model across iterations. We encourage the readers to try and come up with different solutions, and provide us with your feedback for future blogs.

    Business and data understanding

    The first step of the iterative process includes understanding and gathering the data needed to train and test our model later.

    Flights data — We gathered 2016 flights data from the US Bureau of Transportation Statistics website. The website allows us to export one month at a time, so we ended up with 12 csv (comma separated value) files. We used IBM SPSS Modeler to merge all the csv files into one set and to select the ten airports in our scope. Some data clean-up and formatting was done to validate dates and hours for each flight, as seen in Figure 1.

    Figure 1 — gathering and preparing flights data in IBM SPSS Modeler

    Weather data — From the Extensions Hub, we added the TWCHistoricalGridded extension to SPSS Modeler, which made the extension available as a node in the tool. That node took a csv file listing the 10 airports latitude and longitude coordinates as input, and generated the historical hourly data for the entire year of 2016, for each airport location, as seen in Figure 2.

    Figure 2 — gathering and preparing weather data in IBM SPSS Modeler

    Combined flights and weather data — To each flight in the first data set, we added two new columns: ORIGIN and DEST, containing the respective airport codes. Next, flight data and the weather data were merged together. Note: the “stars” or SPSS super nodes in Figure 3 are placeholders for the diagrams in Figures 1 and 2 above.

    Figure 3 — combining flights and weather data in IBM SPSS Modeler Data preparation, modeling, and evaluation

    We iteratively performed the following steps until the desired model qualities were reached:

    · Prepare data

    · Perform modeling

    · Evaluate the model

    · Repeat

    Figure 4 shows the first and second iterations of our process in IBM SPSS Modeler.

    Figure 4 — iterations: prepare data, run models, evaluate — and do it again First iteration

    To start preparing the data, we used the combined flights and weather data from the previous step and performed some data cleanup (e.g. took care of null values). In order to better train the model later on, we filtered out rows where flight cancellations were not related to weather conditions (e.g. cancellations due to technical issues, security issues, etc.)

    Figure 5 — imbalanced data found in our input data set

    This is an interesting use case, and often a hard one to solve, due to the imbalanced data it presents, as seen in Figure 5. By “imbalanced” we mean that there were far more non-cancelled flights in the historical data than cancelled ones. We will discuss how we dealt with imbalanced data in the following iteration.

    Next, we defined which features were required as inputs to the model (such as flight date, hour, day of the week, origin and destination airport codes, and weather conditions), and which one was the target to be generated by the model (i.e. predict the cancellation status). We then partitioned the data into training and testing sets, using an 85/15 ratio.

    The partitioned data was fed into an SPSS node called Auto Classifier. This node allowed us to run multiple models at once and preview their outputs, such as the area under the ROC curve, as seen in Figure 6.

    Figure 6 — models output provided by the Auto Classifier node

    That was a useful step in making an initial selection of a model for further refinement during subsequent iterations. We decided to use the Random Trees model since the initial analysis showed it has the best area under the curve as compared to the other models in the list.

    Second iteration

    During the second iteration, we addressed the skewedness of the original data. For that purpose, we chose one of the SPSS nodes called SMOTE (Synthetic Minority Over-sampling Technique). This node provides an advanced over-sampling algorithm that deals with imbalanced datasets, which helped our selected model work more effectively.

    Figure 7 — distribution of cancelled and non-cancelled flights after using SMOTE

    In Figure 7, we notice a more balanced distribution between cancelled and non-cancelled flights after running the data through SMOTE.

    As mentioned earlier, we picked the Random Trees model for this sample solution. This SPSS node provides a model for tree-based classification and prediction that is built on Classification and Regression Tree methodology. Due to its characteristics, this model is much less prone to overfitting, which gives a higher likelihood of repeating the same test results when you use new data, that is, data that was not part of the original training and testing data sets. Another advantage of this method — in particular for our use case — is its ability to handle imbalanced data.

    Since in this use case we are dealing with classification analysis, we used two common ways to evaluate the performance of the model: confusion matrix and ROC curve. One of the outputs of running the Random Trees model in SPSS is the confusion matrix seen in Figure 8. The table shows the precision achieved by the model during training.

    Figure 8 — Confusion Matrix for cancelled vs. non-cancelled flights

    In this case, the model’s precision was about 95% for predicting cancelled flights (true positives), and about 94% for predicting non-cancelled flights (true negatives). That means, the model was correct most of the time, but also made wrong predictions about 4–5% of the time (false negatives and false positives).

    That was the precision given by the model using the training data set. This is also represented by the ROC curve on the left side of Figure 9. We can see, however, that the area under the curve for the training data set was better than the area under the curve for the testing data set (right side of Figure 9), which means that during testing, the model did not perform as well as during training (i.e. it presented a higher rate of errors, or higher rate of false negatives and false positives).

    Figure 9 — ROC curves for the training and testing data sets

    Nevertheless, we decided that the results were still good for the purposes of our discussion in this blog, and we stopped our iterations here. We encourage readers to further refine this model or even to use other models that could solve this use case.

    Deploying the model

    Finally, we deployed the model as a REST API that developers can call from their applications. For that, we created a “deployment branch” in the SPSS stream. Then, we used the IBM Watson Machine Learning service available on IBM Bluemix here. We imported the SPSS stream into the Bluemix service, which generated a scoring endpoint (or URL) that application developers can call. Developers can also call The Weather Company APIs directly from their application code to retrieve the forecast data for the next day, week, and so on, in order to pass the required data to the scoring endpoint and make the prediction.

    A typical scoring endpoint provided by the Watson Machine Learning service would look like the URL shown below.

    https://ibm-watson-ml.mybluemix.net/pm/v1/score/flights-cancellation?accesskey=<provided by WML service>

    By passing the expected JSON body that includes the required inputs for scoring (such as the future flight data and forecast weather data), the scoring endpoint above returns if a given flight is likely to be cancelled or not. This is seen in Figure 10, which shows a call being made to the scoring endpoint — and its response — using an HTTP requester tool available in a web browser.

    Figure 10 — actual request URL, JSON body, and response from scoring endpoint

    Notice in the JSON response above that the deployed model predicted this particular flight from Newark to Chicago would be 88.8% likely to be cancelled, based on forecast weather conditions.

    Conclusion

    IBM SPSS Modeler is a powerful tool that helped us visually create a solution for this use case without writing a single line of code. We were able to follow an iterative process that helped us understand and prepare the data, then model and evaluate the solution, to finally deploy the model as an API for consumption by application developers.

    Resources

    The IBM SPSS stream and data used as the basis for this blog are available on GitHub. There you can also find instructions on how to download IBM SPSS Modeler, get a key for The Weather Channel APIs, and much more.


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