HEART DISEASE PREDICTION

Background/Problem Statement:

Globally, Heart Disease has become a major cause of morbidity. The deaths are rising significantly annually due to this disease. Of all heart diseases, coronary heart disease (heart attack) is the most common and fatal. The silver lining is that these heart attacks can be highly preventable by maintaining healthy lifestyle (such as reducing alcohol and tobacco use, eating healthily and exercising) coupled with early treatment that greatly improves its prognosis. It is, however, difficult to identify high risk patients because of the multi-factorial nature of several contributory risk factors such as diabetes, high blood pressure, high cholesterol, etc. This is where machine learning and data analytics can be used to analyse the co-relation between factors/parameters and predict the risk of heart disease. The objective of the project is to predict the risk of heart attacks using MinskyTM Machine Learning Models that can help clinically in analysing the risk factors of the disease and interpretation of the important factors affecting the particular patience.

Solution Overview:

After a comprehensive evaluation of the parameters corresponding to the patient’s clinical data, we used Minsky to accurately model the historical data. This comprised of Patient’s age, Gender, Cholesterol Levels, thalach, Chest pain type, Blood Pressure, trestbps etc. Once the detailed modelling was performed by Minsky for the selected Algorithms, historical data used by MinskyTM was separately analysed to predict the Heart attack based on various clinical parameters.

Key Benefits (Minsky):

• User-Friendly cloud-based AI platform
• Scalable across various domains/data.
• Provides you a list of % dependency features that can be used to optimize the outcomes.
• Ability to fine tune or optimize the models by trying different algorithms / prediction attributes
• Easy integration with other third-party solutions such as TABLEAU for data visualization

Project Results:

Benefits:

• Early detection of disease and treatment strategy can save human lives.
• Better visibility on analysis and the root cause of the disease.
• Faster and Reliable predictions on cardiovascular outcomes to provide timely treatments.