Abstract
Abstract: Technology has altered the health arena to a large extent in this era of IT. The goal of this research is to create a diagnosis model for a variety of diseases based on their symptoms. To create such a model, this system used data mining techniques such as classification. The intelligent agent is trained using datasets containing copious data regarding patient diseases that have been gathered, refined, categorised, and utilised. K-Fold cross-validation is used to evaluate the machine learning models after splitting the data. For cross-validation, employed are the Support Vector Classifier, Gaussian Naive Bayes Classifier, and Random The patient might then contact the doctor for further therapy based on the results. It's an example of how technology and medical expertise are flawlessly woven together with the goal of achieving "prediction is better than cure." Keywords: Gaussian Naive Bayes classifier, K-cross validation, Random forest classifier, Support vector classifier, medical data, classification, data mining.
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