• Jenifer Shylaja M
• A Thanam
• Lokayya Deepthi
• Madireddy Khyathi
• Rakshana K

Heart disease is a prevalent and intricate health issue affecting numerous individuals globally, necessitating timely and effective identification, especially in cardiology. Our study employs Fisher Score and Chi-Square as feature selection methods to rank and eliminate irrelevant or redundant attributes. Following feature selection, we employ Support Vector Machine (SVM) and Logistic Regression algorithms for classification. Chi-Square, a mathematical procedure, condenses correlated attributes into fewer, correlated variables, termed principal components, offering a simple means of determining relevant variables. Fisher Score, a widely utilized supervised feature selection method, ranks variables based on Fisher’s score in descending order, facilitating variable selection based on case requirements. Chi-Square is particularly useful when dealing with continuous variables. Assuming a target variable is chosen, we assess each parameter to determine if the Chi-Square technique identifies any relationship with the target. Our project proposes an efficient and accurate system for heart disease diagnosis based on machine learning techniques.

Heart disease, Fisher Score, Chi-Square, Feature selection, Support Vector Machine (SVM), Logistic Regression, Principal components, Correlated attributes, Supervised feature selection, Continuous variables, Target variable, Machine learning techniques, Diagnosis, Efficiency, Accuracy

"EXPLORING MACHINE LEARNING ALGORITHMS FOR HEART DISEASE PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.p155-p163, May-2024, Available :http://www.jetir.org/papers/JETIR2405G24.pdf

"EXPLORING MACHINE LEARNING ALGORITHMS FOR HEART DISEASE PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppp155-p163, May-2024, Available at : http://www.jetir.org/papers/JETIR2405G24.pdf