• Sali Nikitha
• Mr.P.Yugundhar Reddy
• Dr.K.Chaitanya

: In the present day, there is a growing incidence of health issues, primarily attributed to lifestyle factors and hereditary factors. Notably, heart disease has become increasingly prevalent, posing a significant risk to people's lives. This document presents a survey of various classification methods employed to assess the risk level of individuals based on factors like age, gender, blood pressure, cholesterol, and pulse rate. The "Disease Prediction" system relies on predictive modeling to anticipate a user's health condition based on the symptoms provided as input to the system. The system evaluates the user's input symptoms and produces the likelihood of a specific disease as an output. Disease prediction is accomplished through the implementation of three techniques: Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree Algorithms. These methods compute the probability of the disease, resulting in an average prediction accuracy of approximately 81%.

Decision Tree, Naive Bayes, KNN, Heart Disease Prediction

"ENHANCING HEART DISEASE PROGNOSIS WITH MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 10, page no.h529-h535, October-2023, Available :http://www.jetir.org/papers/JETIR2310666.pdf

"ENHANCING HEART DISEASE PROGNOSIS WITH MACHINE LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 10, page no. pph529-h535, October-2023, Available at : http://www.jetir.org/papers/JETIR2310666.pdf