• Sravan Kumar Gurram
• Arathi Chitla
• Harshith Raj Boddu

Heart disease is one of the complex diseases and globally many people are suffering from this disease. Early detection of heart disease can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. The correct prediction of heart disease can prevent life threats. Machine Learning is one of the prominent techniques used in healthcare domain. Many of the machine learning techniques were even working on identifying heart diseases. The logistic regression algorithm is employed as a predictive modeling tool to analyze and interpret the complex relationships within this dataset, ultimately yielding a robust and interpretable model for CVD risk assessment. To achieve the accuracy, we used Logistic Regression with UCI Cleveland data set with different splits.

Cardiovascular diseases, UCI Cleveland data set, logistic regression, machine learning

"Prediction of Cardiovascular diseases using Logistic regression: Machine Learning Classification Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.h284-h288, January-2024, Available :http://www.jetir.org/papers/JETIR2401727.pdf

"Prediction of Cardiovascular diseases using Logistic regression: Machine Learning Classification Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. pph284-h288, January-2024, Available at : http://www.jetir.org/papers/JETIR2401727.pdf