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Analytical model to predict diabetic patients using an optimized hybrid classifier

  • Foundation, algebraic, and analytical methods in soft computing
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Abstract

Diabetes is the most common disease and is a major cause for blindness, kidney failure, heart attacks, stroke and lower limb amputation. Thus, early prediction of diabetes is very crucial to initiating proper treatment to avoid further serious complications of the disease. The performance of recent diabetes detection schemes based on clinical data is highly influenced by low feature distinctiveness and unwanted features such as dermatologic manifestations. Different machine learning classifiers need tedious hyper-parameter tuning, which fails to assure a better diabetes detection rate. This article presents an analytical model to detect diabetes based on an optimized Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Random Forest (RF) using decision level fusion to improve the diabetes detection rate. The hyper-parameters SVM, KNN, and RF are optimized using a multi-objective function-based Particle Swarm Optimization (PSO) algorithm, which considers various clinical entities for the diabetes detection, such as age, body mass index (BMI), blood pressure (BP), glucose, insulin, number of pregnancies, skin thickness, and diabetes pedigree function. The extensive experiments on the Indian Pima diabetes dataset confirmed that the diabetes detection using hybrid classifiers can provide a better prediction rate (94.27%) compared with single classifiers and the previous state of arts.

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Data availability statement

All data presented in this work have been obtained from PIMA Indian diabetes dataset repository at Kaggle. The dataset used in the analysis of the work carried out is available with the authors which can be obtained upon request.

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Acknowledgements

Prof. (Dr.) Poonkuntran Shanmugam, Professor & Dean VIT Bhopal University, Sehore, India. Supervises this research work. Author thanks her for the continuous guidance and timely help. Author also thanks Management, Director and Head of Institution and other technical staff of VIT Bhopal University, Sehore, India, for providing infrastructure and other necessary support for this research work.

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. School of Computing Science and Engineering, VIT Bhopal University, Sehore, Madhya Pradesh, 466114, India

    Jayanta Kiran Shimpi & Poonkuntran Shanmugam

  2. School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632 014, India

    Albert Alexander Stonier

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  1. Jayanta Kiran Shimpi
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  2. Poonkuntran Shanmugam
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All the authors equally contributed in the formulation of manuscript and the work involved.

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Correspondence to Albert Alexander Stonier.

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Shimpi, J.K., Shanmugam, P. & Stonier, A.A. Analytical model to predict diabetic patients using an optimized hybrid classifier. Soft Comput 28, 1883–1892 (2024). https://doi.org/10.1007/s00500-023-09487-w

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