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Volume 11, Issue 1 (9-2020) |
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Hybrid Method of Logistic Regression and Data Envelopment Analysis for Event Prediction: A Case Study (Stroke Disease)
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Jafar Pourmahmoud *    , Maedeh Gholam Azad |
| University of Azarbaijan Shahid madani, Tabriz, Iran , Pourmahmoud@azaruniv.ac.ir |
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Abstract: (5151 Views) |
Predictive analytics is an area of statistics that deals with extracting information from data and using
that to predict trends and behavioral patterns. Many mathematical models have been developed and
used for prediction, and in some cases, they have been found to be very strong and reliable. This
paper studies different mathematical and statistical approaches for events prediction. The main goal
of this research is to design and construct a hybrid prediction method for events prediction, based on
Logistic Regression (LR) method and Data Envelopment Analysis (DEA) technique. In this study, a
novel hybrid algorithm was developed, and considering the kind of collected data, LR method was
applied for input selection, and the capability of the additive (ADD) model of DEA was examined to
predict the occurrence or non-occurrence of the events. To apply the proposed approach, the selected
disease for the case study was a stroke. The results showed that any patient who was placed on the
frontier has had a stroke by one or more risk factors. On the other hand, the observations that were
not on the frontier had not suffered from a stroke. The overall accuracy of 88.5 percentages was
obtained for the developed method
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| Keywords: Data Envelopment Analysis, Logistic Regression, Additive Model, Risk Factor, Stroke Disease. |
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Full-Text [PDF 331 kb]
(8892 Downloads)
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Type of Study: Original |
Subject:
Mathematical Modeling and Applications of OR
Received: 2021/06/22 | Accepted: 2020/09/20 | Published: 2020/09/20
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