Abstract
Machine learning has changed healthcare in the modern world by making it possible to predict diseases accurately and on time. Being able to predict a number of diseases at the same time can help with early detection and response, which would lead to better patient outcomes and lower healthcare costs. This paper examines the application of machine learning algorithms for the identification of diverse conditions, emphasizing their advantages, constraints, and future potential. It gives a detailed look at some of the most important machine learning environments and data sets that are used to predict diseases. It also stresses how important it is to choose the right elements, test how well the model works, and combine data from different sources to get more reliable predictions of the disease.
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CITATION STYLE
Singh, A. P., Sagar, A., Verma, D., Srivastava, V., & Ranjan, R. (2026). Disease Prediction using Machine Learning. In 17th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2026 (Vol. 2, pp. 4374–4379). Grenze Scientific Society. https://doi.org/10.54646/bijcs.2022.11
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