This project focuses on predicting the likelihood of heart disease in individuals based on specific medical parameters. Utilizing a logistic regression model, the system evaluates the risk of heart disease by analyzing critical factors such as ST depression, ST slope, and the number of major vessels. The logistic regression algorithm, known for its effectiveness in binary classification problems, helps determine whether a person is likely to be normal or at risk of heart disease.
- Input Parameters: The model requires inputs including ST depression, ST slope, and the number of major vessels. These parameters are crucial in assessing the heart's condition and play a significant role in the prediction process.
- Logistic Regression Model: Logistic regression is employed due to its robustness and simplicity in binary classification tasks. It helps in estimating the probability of the presence or absence of heart disease.
- Prediction Output: Based on the input parameters, the model provides a clear prediction indicating whether the individual is at risk of heart disease or not. This prediction can be instrumental in early diagnosis and preventive measures.
The project aims to assist healthcare professionals and individuals in early detection and management of heart disease risk. By leveraging key medical data, the system offers a reliable method to assess heart health, potentially leading to timely medical intervention and improved patient outcomes.