Cardiovascular diseases are one of the biggest reasons for death of millions of people around the world only second to cancer. A heart attack occurs when a blood clot blocks the blood flow to a part of the heart. In case this blood clot cuts off the blood flow entirely, the part of the heart muscle begins to die as a result. Going by the statistics, a heart problem can gradually start between the age of 40–50 for people with unhealthy diet and bad lifestyle choices. So, an early prognosis can really make a huge difference in their lives by motivating them towards a healthy and active life. By changing their lifestyle and diet this risk can be controlled. This Project intends to pinpoint the most relevant/risk factors of heart disease as well as predict the overall risk using machine learning. The machine learning model predicts the likelihood of patients getting a heart disease trained on dataset of other individuals. As the result, the probability of getting a heart disease based on current lifestyle and diet is calculated. The model was trained with Framingham heart study dataset.
Heart Disease, Machine Learning, logistic regression, Cross-validation
