K. J. Somaiya

Heart disease is one of the most critical disease to cure in health care sector. People above age 50 and above have possibility of facing so many diseases that might be linked with heart. But some facts have claimed that recent lifestyle has made people in their adolescence also stand a chance of getting heart disease. Being said heart disease is critical disease also the data generated from patients are in terabytes, this data can be helpful in making radical decisions. We can gain good results by using various machine learning techniques. In this paper we will study about a Heart Disease Prediction Software (HDPS) which is developed using Hybrid Algorithm including Linear Logistic and KNN for analyzing the presences of heart disease. The system uses 15 medical parameters such as age, blood pressure, sex, cholesterol, etc. for prediction. The HDPS will predict likelihood of patients having heart disease. The obtained results will be illustrating that the designed diagnostic system can effectively predict the heart disease. If the user is found out of having the disease then it suggests the nearest Cardiologist or Physician’s Clinic for Treatment using Google API.