Predicting The Risk Of Heart Disease Using Advanced Machine Learning Approach
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 7, Pages 1638-1645
AbstractHeart diseases also called Cardiovascular Diseases (CVD) include range of conditions portraying illness of heart. These include diseases related to blood vessels, rhythm problem, chest pain, heart attack, strokes, and fluctuating blood pressure. Person suffering with CVD has fluctuating blood flow rate. CVD are the leading cause of mortality in India including both male and female. A quarter of all mortality is attributed to cardiovascular diseases. Heart diseases and strokes are the pre-dominant causes and are responsible for > 80% of CVD deaths. Therefore in this paper a machine learning model is implemented on the dataset downloaded from kaggle. This dataset contains various parameters contributing to cardiac morbidity. It contains 70000 records and contains parameters like age, cholesterol, glucose, smoking, alcoholic habit etc. The decision Tree model is used fot training and predicting the risk of heart disease. The accuracy of implemented model is 73%.
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