• G. M. Pushpanjali
• Pratik R. Ugral

The objective of this research is to develop a machine learning model that uses various algorithms to predict cardiac disease. The dataset contains patient data and relevant medical characteristics. We were able to attain an accuracy of up to 86% by preprocessing the data, dividing it into training and testing sets, and fine-tuning the hyperparameters. The findings show the potential of innovative machine learning methods in medical diagnosis, offering healthcare practitioners a useful tool for the early identification and prevention of cardiac disease. Keywords: hyperparameter tweaking, random forest classifier, medical diagnostics, machine learning, and heart disease prediction.

hyperparameter tweaking, random forest classifier, medical diagnostics, machine learning, and heart disease prediction.

"Cardio Predict: Machine Learning-Powered Heart Disease Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.g398-g403, October-2024, Available :http://www.jetir.org/papers/JETIR2410648.pdf

"Cardio Predict: Machine Learning-Powered Heart Disease Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppg398-g403, October-2024, Available at : http://www.jetir.org/papers/JETIR2410648.pdf