Problem Description:
The goal of this project is to build a robust and accurate predictive model that utilizes supervised learning techniques to classify whether an individual is at risk of developing heart disease based on parameters influencing heart rate. The model will be trained on a dataset containing various physiological and lifestyle parameters such as age, gender, blood pressure, cholesterol levels, smoking habits, and exercise patterns, among others. The aim is to identify patterns and relationships between these parameters and the presence or absence of heart disease.
The developed model should be able to take as input the relevant parameters of an individual and accurately classify whether they are likely to have heart disease. This predictive capability can potentially aid medical professionals in making early interventions and providing appropriate treatment to individuals at high risk of developing heart disease.
The success of this project will be measured based on the model's accuracy, precision, recall, and F1-score in classifying heart disease cases. The model's performance will be evaluated using suitable evaluation metrics, and the best performing model will be selected for deployment in real-world scenarios to assist healthcare practitioners in making informed decisions.
The proposed project aiams to contribute to the field of cardiovascular health by leveraging supervised learning techniques to develop an efficient and reliable heart disease prediction system, ultimately leading to improved patient outcomes and reduced mortality rates associated with heart-related conditions.