Heart Disease Prediction System Using SVM
Rutuja Prakash Sheral, Sneha Sudhir Dange, Priyanka Sanjay Gaikwad, Pratiksha Vilas Shewale, Dr Sulochana Sonkamble
heart disease, machine learning, real-time, support vector machine
Coronary Heart Disease (CHD) is the most common type of heart disease, killing over 370,000 people annually. Every year about 735,000 Americans has a heart attack. Of these, 525,000 are a first heart attack and 210,000 happen in people who have already had a heart attack. This makes heart disease a major concern to be dealt with. But it is difficult to identify heart disease because of several risk factors such as diabetes, high blood pressure, high cholesterol, abnormal pulse rate, and many other factors. The healthcare domain is one of the prominent research fields in the current scenario with the rapid improvement of technology and data. It is difficult to handle the huge amount of data of the patients. There are a lot of procedures for the treatment of multiple diseases across the world. Machine Learning is an emerging approach that helps in prediction, diagnosis of a disease. This system depicts the prediction of disease based on symptoms using machine learning. The system analyzes the symptoms provided by the user as input and gives the probability of the disease as an output. Machine Learning algorithms such as Support Vector Machine, is employed on the provided dataset and predict the disease. Its implementation is done through the python programming language. The research demonstrates the best algorithm based on their accuracy. The accuracy of an algorithm is determined by the performance on the given dataset.
Article Details
Unique Paper ID: 154908

Publication Volume & Issue: Volume 8, Issue 12

Page(s): 574 - 578
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