Md. Imdadul Islam

Work place: Department of Computer Science & Engineering, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh



Research Interests: Deep Learning, Machine Learning


Md. Imdadul Islam has completed his B.Sc. and M.Sc Engineering in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology, Dhaka, Bangladesh in 1993 and 1998 respectively and has completed his Ph.D degree from the Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh in the field of network traffic in 2010. He is now working as a Professor at the Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka, Bangladesh. Previously, he worked as an Assistant Engineer in Sheba Telecom (Pvt.) LTD (A joint venture company between Bangladesh and Malaysia, for Mobile cellular and WLL), from Sept.1994 to July 1996. Dr Islam has a very good field experience in installation and design of mobile cellular network, Radio Base Stations and Switching Centers for both mobile and WLL. His research field is network traffic, wireless communications, wavelet transform, adaptive filter theory, ANFIS, neural network, deep learning and machine learning. He has more than two hundred research papers in national and international journals and conference proceedings.

Author Articles
Detection of Diabetes using Combined ML Algorithm

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

DOI:, Pub. Date: 8 Feb. 2024

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.

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