Abstract
This article introduces a method for classifying diabetes based on machine learning (ML) methods. In recent years, significant focus have been put onto increasing disease classification performance through the use of ML approaches. This paper outlines the use of five interpretable ML algorithms: Bagging classifier, Random Forest, AdaBoost, Multilayer Perceptron, and Restricted Boltzmann Machine. All the ML classifiers were trained and tested in a benchmark Biostat Diabetes Dataset using Python programming. Each technique’s performance is evaluated to discover which has the finest accuracy, precision, recall, F1-score, specificity, and sensitivity. Experimental findings and assessment reveal that the Random Forest technique outperforms all other ML techniques by achieving 98% precision, 98% recall, 98% F1-score, 75% sensitivity, 96% specificity, and accuracy of 97.5%.
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Ashisha, G.R., Mary, X.A., Chowdhury, S., Karthik, C., Choudhury, T., Kotecha, K. (2024). Early Detection of Diabetes Using ML Based Classification Algorithms. In: Garg, D., Rodrigues, J.J.P.C., Gupta, S.K., Cheng, X., Sarao, P., Patel, G.S. (eds) Advanced Computing. IACC 2023. Communications in Computer and Information Science, vol 2054. Springer, Cham. https://doi.org/10.1007/978-3-031-56703-2_12
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DOI: https://doi.org/10.1007/978-3-031-56703-2_12
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