An Effective Feature Selection for Type II Diabetes Prediction

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Kakavand Teimoory Ghazaleh; KEYVANPOUR MOHAMMAD REZA

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Seminar Paper

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Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

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Title

An Effective Feature Selection for Type II Diabetes Prediction

Author(s)

Kakavand Teimoory Ghazaleh | KEYVANPOUR MOHAMMAD REZA

Keywords

Diabetes Prediction

Supervised Algorithms

Machine Learning

Pima Dataset

Abstract

Diabetes is a condition related to metabolism that arises due to various reasons. Each year, a significant portion of patients experience life-threatening complications leading to fatality. This illness is classified into three distinct forms: Type 1, Type 2, and Gestational diabetes. It is of utmost importance to predict type 2 diabetes, which arises from cellular insulin utilization deficiency or secretion disorder, as it enables the prevention of complications or delays in the onset of the disease. Predicting the occurrence of illnesses using Machine Learning and artificial intelligence can substantially mitigate its costs. However, due to interpretability challenges in proposed models, physicians and patients are hesitant to adopt them. Previous studies have utilized various algorithms including Naï, ve Bayes, SVM, KNN, and decision tree algorithms for patients’,classification. In this paper, conducted on the Pima Dataset, we employed a preprocessing method utilizing the most significant features selected by the Random Forest algorithm. Additionally, for model testing, we utilized the SVM algorithm, known for its high discriminative power in binary classification tasks and its relatively good interpretability. The results indicate that the proposed model achieved an accuracy of 80. 09%, outperforming other models by a 2. 26% improvement. Furthermore, there were notable improvements in precision and specificity metrics with the proposed model. By utilizing these methods, web-based applications can be employed to motivate physicians and patients for Diabetes Prediction.

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APA: Copy

Kakavand Teimoory, Ghazaleh, & KEYVANPOUR, MOHAMMAD REZA. (2024). An Effective Feature Selection for Type II Diabetes Prediction. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147366/en

Vancouver: Copy

Kakavand Teimoory Ghazaleh, KEYVANPOUR MOHAMMAD REZA. An Effective Feature Selection for Type II Diabetes Prediction. 2024. Available from: https://sid.ir/paper/1147366/en

IEEE: Copy

Ghazaleh Kakavand Teimoory, and MOHAMMAD REZA KEYVANPOUR, “An Effective Feature Selection for Type II Diabetes Prediction,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147366/en

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