Heart Disease Prediction Using Logistic Regression Machine Learning Model

Heart disease is a significant global health issue responsible for millions of deaths annually. Modifiable risk factors such as high cholesterol, smoking, physical inactivity, and high blood pressure can be tackled through lifestyle changes and medical interventions. Machine learning and artificial intelligence have the potential to enhance disease prediction and management, leading to better outcomes for patients. Python, along with its libraries such as Scikit-Learn and TensorFlow, provides a versatile platform for developing and deploying machine learning models. In this study, the logistic regression model from Scikit-Learn was employed to predict the likelihood of cardiovascular disease based on various risk factors. The Pickle library was used to store the trained model for future use. Future research could enhance the model’s efficacy by developing a user-friendly graphical user interface, implementing more advanced machine learning techniques, expanding the database used for training, and incorporating additional risk factors. The study demonstrates the potential of machine learning models for predicting and managing cardiovascular disease, highlighting the need for further research and development to improve accuracy, applicability, and clinical utility. The development of effective prevention and treatment strategies using advanced technologies such as AI and machine learning is crucial in reducing the impact of this disease on individuals and society as a whole.