Abstract
Making informed decisions and precise predictions is made possible by machine learning (ML). In order to detect and predict diseases including heart attacks, diabetes, breast cancer, chronic kidney disease, and COVID-19 in humans using numerous risk factors, classification models like Logistic Regression, Random Forest Classifier, Support Vector Machine, and Decision Tree Classifier are used. The datasets are categorized according to medical characteristics, and machine learning algorithms are utilized to process them. With the aid of conventional machine learning techniques, correlations between the various variables included in the dataset are discovered, and these correlations are then effectively utilized in the prediction of diseases. Using the patient's medical history, they can determine if the patient is likely to be diagnosed with a specific disease or not and anticipate the patient's health condition using training from natural events. The outcome of this prediction is whether the patient is likely to be diagnosed with any of the diseases mentioned above.
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Obulesu, O., Venkateswarulu, N., Sri Vidya, M., Manasa, S., Pranavi, K., Brahmani, C. (2023). Early Prediction of Healthcare Diseases Using Machine Learning and Deep Learning Techniques. In: Seetha, M., Peddoju, S.K., Pendyala, V., Chakravarthy, V.V.S.S.S. (eds) Intelligent Computing and Communication. ICICC 2022. Advances in Intelligent Systems and Computing, vol 1447. Springer, Singapore. https://doi.org/10.1007/978-981-99-1588-0_29
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DOI: https://doi.org/10.1007/978-981-99-1588-0_29
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