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Welcome! This project is a successful MLOps project implemented by Meet, Tirth and Amitesh.
Heart disease prediction is a crucial aspect of preventive healthcare that involves the comprehensive analysis of diverse data points to evaluate an individual's susceptibility to cardiovascular diseases. This process integrates demographic details like age and gender with critical clinical information, including medical and family histories, lifestyle choices, and existing health conditions such as hypertension or diabetes. By examining biomarkers like blood pressure, cholesterol levels, and blood sugar, alongside results from medical tests and imaging studies, predictive models can identify patterns and trends indicative of potential heart issues. Machine learning algorithms play a pivotal role in processing this information, helping stratify individuals into risk categories. The ultimate goal is to enable timely interventions and personalized preventive strategies, empowering individuals to make lifestyle adjustments that can mitigate the risk of heart-related events like heart attacks or strokes. Continuous monitoring and updating of predictive models ensure ongoing accuracy and effectiveness in supporting proactive heart health management.
This dataset gives information related to heart disease. The dataset contains 13 columns, target is the class variable which is affected by the other 12 columns. Here the aim is to classify the target variable to (disease\non disease) using different machine learning algorithms and find out which algorithm is suitable for this dataset.
Age Gender Chest Pain Type Resting Blood Pressure Serum Cholesterol Fasting Blood Sugar Resting Electrocardiographic Results Maximum Heart Rate Achieved Exercise-induced angina Depression induced by exercise relative to rest. Slope of the Peak Exercise ST Segment Number of Major Vessels Colored by Fluoroscopy Thalassemia Target
Error Matrix Comparision of Model With Different Parameters along With Comparision od Different Attributes
A flask backend routing with an simple ui to accept the new data from users and send it to the particular endpoints
