Heart Disease Prediction
Khan Nasreen, Mhaske Utkarsha, Nimbalkar Rutuja , Gite Pratiksha
Khan Nasreen Computer Engineering & Anantrao Pawar College Of Engineering And Research
Mhaske Utkarsha Computer Engineering & Anantrao Pawar College Of Engineering And Research
Nimbalkar Rutuja Computer Engineering & Anantrao Pawar College Of Engineering And Research
Gite Pratiksha Computer Engineering & Anantrao Pawar College Of Engineering And Research
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Abstract - Heart disease (HD) is one of the most common diseases nowadays, and an early diagnosis of such a disease is a crucial task for many health care providers to protect their patients from such a disease and to save lives. The treatment cost of heart disease is not affordable for most of the patients. So we can reduce this problem by a Heart Disease Prediction System (HDPS). It is helpful for earlier diagnosis of heart disease The system can predict the likelihood of patients getting a heart disease by using medical profiles such as age, sex, blood pressure, cholesterol and blood sugar. Also, the performance will be compared by calculation of confusion matrix. This can help to calculate accuracy, precision, and recall. The overall system provides high performance and better accuracy. The heart is one of the main organs of the human body. It pumps blood through blood vessels of the circulatory system. The circulatory system is extremely important because it takes care of activities like transportation of blood, oxygen and other materials to the different organs of the body. Heart plays the most crucial role in the circulatory system. If the heart does not function properly then it will lead to serious health conditions sometimes even to death. The diagnosis of heart disease is based on signs, symptoms and physical examination of the patient. There are several factors that increase the risk of heart disease, such as smoking habit, body cholesterol level, family history of heart disease, obesity, high blood pressure and lack of physical exercise. The existing system does not manage the clinical details. Existing system leads to failure in case of any inconsistencies and missing data. The current automated system is used to identify the key patterns or features from the medical data by using the main classifier model.
Key Words: Cleveland Heart Disease Database, Decision Trees, Random forest, Hybrid algorithm, Machine learning
