Data Visualisation Models for Analytics Use Artificial Intelligence to Predict Diabetes in Women
Hussein Z Almngoshi
Department of Computer Science, College of Computer Science and Information Technology, University of Kerbala, Karbala, Iraq, Department of Information Technology, College of Science, University of Warith Al-Anbiyaa, Karbala, Iraq.
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
Department of Computer Science and Engineering CSE-Aritifical Intelligence and Machine Learning, Kallam HaranadhaReddy Institute of Technology (KHIT), Dasaripalem, Andhra Pradesh, India.
Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Identifying and classifying diabetes problems among women can be achieved using several Machine Learning (ML) algorithms. This paper additionally includes a summary of the evaluation of the performance of these MLs with algorithms on many different classification metrics. The AUC-ROC score is the best for Extreme Gradient Boost (XGB) with 85%, followed by SVM and Decision Trees (DT). Logistic Regression (LR) is showing low performance. However, the DT and XGB show promising performance against all the classification metrics. However, the SVM shows a lower support value; hence, it cannot be claimed to be a precious classifier. A study reveals that women are four times more susceptible to diabetic conditions than men. But the healthcare systems do not give special attention to diabetic conditions in women. This study proposes to predict the probability of diabetes in females based on numerous medical conditions they may have. The ML accurately predicts diabetic complications based on biological conditions such as blood glucose levels, age, Body Mass Index (BMI), numerous pregnant women, and other factors.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Hussein Z Almngoshi, Mithra C, Abburu Srirama Kanaka Ratnam, Subhashini Shanmugam, Saravanan V and Bhaskar Marapelli;
Methodology: Hussein Z Almngoshi, Mithra C and Abburu Srirama Kanaka Ratnam;
Software: Subhashini Shanmugam, Saravanan V and Bhaskar Marapelli;
Data Curation: Mithra C and Abburu Srirama Kanaka Ratnam;
Writing- Original Draft Preparation: Hussein Z Almngoshi, Mithra C and Abburu Srirama Kanaka Ratnam;
Visualization: Abburu Srirama Kanaka Ratnam, Subhashini Shanmugam, Saravanan V and Bhaskar Marapelli;
Investigation: Hussein Z Almngoshi, Mithra C, Abburu Srirama Kanaka Ratnam, Subhashini Shanmugam and Saravanan V;
Supervision: Mithra C and Abburu Srirama Kanaka Ratnam;
Validation: Hussein Z Almngoshi and Mithra C;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.
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Corresponding author
Subhashini Shanmugam
Department of Computer Science and Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India.
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Cite this article
Hussein Z Almngoshi, Mithra C, Abburu Srirama Kanaka Ratnam, Subhashini Shanmugam, Saravanan V and Bhaskar Marapelli, “Data Visualisation Models for Analytics Use Artificial Intelligence to Predict Diabetes in Women”, Journal of Machine and Computing, vol.5, no.1, pp. 551-560, January 2025, doi: 10.53759/7669/jmc202505043.
