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Comparative Analysis of Machine Learning Algorithms for Early Prediction of Diabetes Mellitus in Women

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Modelling and Implementation of Complex Systems (MISC 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 156))

Abstract

Diabetes is a chronic disease characterized by hyperglycemia where a person suffers from a high level of blood sugar, which leads to complications such as blindness, cardiovascular diseases, and amputation. It is expected that in 2040 the diabetic patients will reach 642 million globally. Hence considering this alarming figure there is a strong need to early diagnose and predict the symptoms of diabetes to save precious human lives. One possible way to diagnose this disease is to leverage machine learning algorithms. Machine learning has swiftly been infiltrating in various domains in healthcare. With the help of diabetes data, machine learning algorithms can find hidden patterns to predict whether a patient is diabetic or non-diabetic. This research aims to provide a comparative analysis of the performance and effectiveness of selected machine learning algorithms in predicting diabetes in women. We develop a predication framework and implemented ten different machine learning algorithms, namely: Naive Bayes, BayesNet, Decision Tree, Random Forest, AdaBoost, Bagging, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Multi-Layer Perceptron. Experimental results procured for the Frankfurt hospital (Germany) dataset shows that K-Nearest Neighbor, Random Forest, and Decision Tree outperformed the other algorithms in terms of all metrics. We believe that our diabetes prediction framework will assist doctors to predict diabetes mellitus with high accuracy.

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Acknowledgement

This work was supported by the Roadway Transportation and Traffic Safety Research Center (RTTSRC) of the United Arab Emirates University (grant number 31R151).

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Authors and Affiliations

  1. College of Information Technology, United Arab Emirates University, Al Ain, UAE

    Sumbal Malik, Saad Harous & Hesham El-Sayed

Authors
  1. Sumbal Malik
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  2. Saad Harous
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  3. Hesham El-Sayed
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Correspondence to Sumbal Malik .

Editor information

Editors and Affiliations

  1. Faculty of New Information and Communication Technologies, University of Constantine 2, Constantine, Algeria

    Salim Chikhi

  2. Faculty of Technology, University of Saida, Saida, Algeria

    Abdelmalek Amine

  3. Faculty of New Information and Communication Technologies, University of Constantine 2, Constantine, Algeria

    Allaoua Chaoui

  4. Faculty of New Information and Communication Technologies, University of Constantine 2, Constantine, Algeria

    Djamel Eddine Saidouni

  5. Faculty of Science and Technology, Department of Mathematics and Computer Science, University of El Oued, El-Oued, Algeria

    Mohamed Khireddine Kholladi

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Malik, S., Harous, S., El-Sayed, H. (2021). Comparative Analysis of Machine Learning Algorithms for Early Prediction of Diabetes Mellitus in Women. In: Chikhi, S., Amine, A., Chaoui, A., Saidouni, D., Kholladi, M. (eds) Modelling and Implementation of Complex Systems. MISC 2020. Lecture Notes in Networks and Systems, vol 156. Springer, Cham. https://doi.org/10.1007/978-3-030-58861-8_7

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  • DOI: https://doi.org/10.1007/978-3-030-58861-8_7

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  • Online ISBN: 978-3-030-58861-8

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