Abstract
Electronic health records, images, and text are a few examples of the types of data collected by healthcare organizations, but it is still difficult to comprehend this information. New machine learning (ML) techniques can be used to reveal hidden patterns that might one day help in diabetes diagnosis in its earliest stages. This work presents a method for predicting diabetes using a number of ML algorithms, such as the support vector classifier (SVC), decision tree classifier (DTC), K-neighbors classifier (KNC), logistic regression (LR), random forest classifier (RFC), AdaBoost classifier (ABC), and gradient boosting classifier (GBC) by analyzing the PIMA Indian dataset. Due to the proposed system, healthcare providers have access to a potent prognostic tool. SVC and RFC have been demonstrated to be the most precise classification strategies for the PIMA Indian Diabetes Dataset (PIDD), with an accuracy of 79.3%.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Rajput, G.G., Alashetty, A. (2023). Diabetes Classification Using ML Algorithms. In: Suma, V., Lorenz, P., Baig, Z. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-99-1624-5_65
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DOI: https://doi.org/10.1007/978-981-99-1624-5_65
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