• Alisha Chittaranjan Pawar
• Shreyas Rajiv Patil
• Suhas Balu Pingat
• Shweta Gabhane

Depending on predictive modelling, the "Smart Health Prediction Using Machine Learning" system forecasts the disease of patients or users based on the symptoms that the user inputs to the system. There are three ways to sign in to the application: user/patient, doctor, and admin. The tool evaluates the user's or patient's symptoms as input and outputs the likelihood of the disease based on the prediction made using the algorithm. The Nave Bayes Classifier is used to make intelligent health predictions. By taking into account all of its features that were trained during the training phase, the Nave Bayes Classifier calculates the disease % probability. An accurate interpretation of disease data aids in early disease prediction for patients and users and gives them a clear understanding of the condition.

Machine Learning, Naïve Bayes, Prediction Analysis, Symptoms.

"Smart Health Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 2, page no.e445-e451, February-2023, Available :http://www.jetir.org/papers/JETIR2302450.pdf

"Smart Health Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 2, page no. ppe445-e451, February-2023, Available at : http://www.jetir.org/papers/JETIR2302450.pdf