Shifat Jahan Setu; Fahima Tabassum; Sarwar Jahan; Md. Imdadul Islam

Detection of Diabetes using Combined ML Algorithm

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Author(s)

Shifat Jahan Setu ¹ Fahima Tabassum ^2,* Sarwar Jahan ³ Md. Imdadul Islam ¹

1. Department of Computer Science & Engineering, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

2. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

3. Department of Computer Science and Engineering at East West University, Dhaka, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2024.01.02

Received: 20 Jul. 2023 / Revised: 17 Sep. 2023 / Accepted: 7 Oct. 2023 / Published: 8 Feb. 2024

Index Terms

FIS, SVM, FCM, Logistic Regression and Combining Algorithm

Abstract

Recently data clustering algorithm under machine learning are used in ‘real-life data’ to segregate them based on the outcome of a phenomenon. In this paper, diabetes is detected from pathological data of 768 patients using four clustering algorithms: Fuzzy C-Means (FCM), K-means clustering, Fuzzy Inference system (FIS) and Support Vector Machine (SVM). Our main objective is to make binary classification on the data table in a sense that presence or absence of diabetes of a patient. We combined the four machine learning algorithms based on entropy-based probability to enhance accuracy of detection. Before applying combining scheme, we reduce the size of variables applying multiple linear regression (MLR) on the table then logistic regression is again applied on the resultant data to keep the outlier within a narrow range. Finally, entropy based combining scheme with some modification is applied on the four ML algorithms and we got the accuracy of detection about 94% from the combining technique.

Cite This Paper

Shifat Jahan Setu, Fahima Tabassum, Sarwar Jahan, Md. Imdadul Islam, "Detection of Diabetes using Combined ML Algorithm", International Journal of Intelligent Systems and Applications(IJISA), Vol.16, No.1, pp.11-23, 2024. DOI:10.5815/ijisa.2024.01.02

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International Journal of Intelligent Systems and Applications (IJISA)