Abstract
HEART ATTACK PREDICTION USING MACHINE LEARNING
Annaji Kuthe*, Achal M. Chavan and Neha S. Lambat
ABSTRACT
Within the current era, Heart Failure (HF) is one of the common diseases that can lead to dangerous situation. Because of this disease almost 26 million of patients are affecting. As per the heart consultant and surgeon?s point of view, it is difficult to predict the heart failure on right time. Classification and predicting models are there, which aid the medical field and can illustrate how to use the medical data efficiently. Since diagnosis could be a sophisticated task and plays a significant role in saving human lives thus it has to be dead accurately and with efficiency however there's lack of effective tools to find hidden relationships and trends in e- health information. This paper aims to improve the Heart Failure prediction accuracy using UCI (Universal Chess Interface) heart disease dataset. We use, various machine learning techniques such as to understand the data and predict the Heart Failure differences in medical database. An appropriate and accurate computer-based automated decision support system is required to reduce the cost for achieving clinical tests. As well the results and relative study showed that, the current work better the previous accuracy score in predicting heart disease. The integration of the machine learning model presented in this study with medical data methodology is beneficial to predict the Heart Failure.
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