Conference Paper2024

Comparative Analysis of Machine Learning Models for Diabetes Prediction: A Cross-Dataset Study of Bangladeshi and Pima Indian Populations

Mahmudul Islam, Syed Tangim Pasha, Jahanggir Hossain Setu, Nabarun Halder, Ashraful Islam, M. Ashraful Amin

2024 7th Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII 2024)

IEEE, pp. 1-5

  • Good Health and Well-being — Relevance: 94%
DOI: 10.1109/ceii65291.2024.00019URL

Abstract

This study examines the increasing incidence of diabetes in Bangladesh and the necessity for effective tools to categorize and forecast diabetic situations utilizing Machine Learning (ML) methodologies. It compares the performance of predictive models on the DiaHealth dataset and the widely used PIMA Indians Diabetes dataset. Two ML algorithms, Random Forest (RF) and Naïve Bayes (NB), have been used to classify diabetic and non-diabetic individuals. This study used the PIMA Indians Diabetes dataset (768 samples) and the Dia-Health dataset from Bangladesh (5,437 samples). Both datasets contain attributes including insulin levels, blood pressure, Body Mass Index (BMI), and age. Models have been evaluated using performance metrics such as accuracy, precision, recall, F1-score, and Receiver Operating Characteristics-Area Under the Curve (ROC-AUC). The RF model achieved 93.93% accuracy on the DiaHealth dataset and 73.38% on the PIMA Indians Diabetes dataset. In contrast, NB performed better on non-diabetic patients in both datasets but struggled to accurately classify diabetic patients, particularly in the DiaHealth dataset. The models showed variance in performance across datasets. This indicates demographic differences and feature distributions. The study shows that RF is effective in predicting diabetes in local Bangladeshi populations but also faces challenges with class imbalance and feature variation. This study also indicates the need for dataset-specific tuning of ML models in diabetes classification and prediction tasks.

Keywords

  • diabetes prediction
  • naive Bayes
  • diabetes classification
  • Bangladeshi population
  • Random forest
  • class imbalance
  • Pima Indians diabetes dataset
  • cross-dataset comparison
  • feature distribution

CCDS Authors

Syed Tangim Pasha
Syed Tangim Pasha

Syed Tangim Pasha

  • Collaborator, HCI Wing

Lecturer

Department of Computing and Information System

Daffodil International University

Published at

UbiComp

Research Interests

Health Informatics, Affective Computing, Digital Health, Wearable Computing

Nabarun Halder
Nabarun Halder

Nabarun Halder

  • Research Assistant, CCDS

Published at

UbiComp, IJCNN, HCII, IEEE Access

Research Interests

Machine Learning, Deep Learning, Natural Language Processing, Health Informatics, Human-Computer Interaction

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

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