Research Paper On Heart Disease Prediction Using Artificial Intelligence and Machine Learning

Research Paper On Heart Disease Prediction Using Artificial Intelligence and Machine Learning

Kiran Pustode1, Tara Shende2, Rahul Bhandekar3, Rahul Navkhare4

1Student of M-Tech Artificial Intelligence & Data Science Engineering Department in WCEM, Nagpur

2,3,4Professor of M-Tech Artificial Intelligence & Data Science Engineering Department in WCEM, Nagpur

ABSTRACT

Heart disease is a leading cause of mortality worldwide, necessitating the development of effective predictive models for early diagnosis and intervention. We propose a logistic regression-based approach to predict heart disease risk using artificial intelligence and machine learning techniques. We utilize a comprehensive dataset containing various clinical parameters such as age, gender, blood pressure, cholesterol levels, and other relevant factors. Feature selection and preprocessing methods are employed to enhance model performance and interpretability. Our results demonstrate the effectiveness of logistic regression in accurately predicting heart disease risk, with performance metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) evaluated. Additionally, we compare the performance of logistic regression with other machine learning algorithms to assess its superiority in this context. Overall, our findings highlight the potential of logistic regression as a valuable tool for heart disease prediction and its relevance in clinical practice. This study utilizes a comprehensive dataset comprising demographic, clinical, and lifestyle factors obtained from a diverse population of individuals. The logistic regression model is trained on this dataset to learn the relationships between these factors and the likelihood of developing heart disease. Feature selection techniques are employed to identify the most informative predictors, enhancing the model's predictive performance and interpretability.

Keywords:  Heart disease prediction, artificial intelligence, machine learning, logistic regression, feature selection.