Abstract
Disease prediction is a method of recognizing patient’s condition using machine and deep learning algorithms just on patient’s care history. The symptoms of various illnesses will vary. The capacity to detect illnesses that a person is likely to contract throughout time is referred to as multi-disease prediction. This illness prediction is crucial in today’s world since most people are predisposed to the same types of chronic diseases. Preventive is always preferable to cure. However, if the prediction is incorrect, the patient’s psychological state may suffer since the person may become tense as a result of expecting that the disease will develop in the future when there is no such possibility. As a result, precision is essential for developing disease models. Certain earlier efforts in this field have limited accuracy, whereas others have just achieved thus for a single or a few disorders. This research proposes a method for predicting seven diseases: diabetes, heart disease, kidney disease, liver disease, breast cancer, malaria, and pneumonia. This article uses flask to create a multi-disease prediction web-app that includes prediction models for the spectrum of clinical above. In this research, deep learning models are used to forecast diabetes, breast cancer, heart, kidney, and liver illnesses, while a deep learning CNN model is utilized to forecast malaria and pneumonia diseases. Those models are anticipated to be extremely accurate.
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Chunduru, A., Kishore, A.R., Sasapu, B.K. et al. Multi Chronic Disease Prediction System Using CNN and Random Forest. SN COMPUT. SCI. 5, 157 (2024). https://doi.org/10.1007/s42979-023-02521-6
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DOI: https://doi.org/10.1007/s42979-023-02521-6