EARLY PREDICTION OF HEART DISEASES USING LOGISTIC REGRESSION ALGORITHM


J. Sophia Jone, S. Kipsy
Department of ECE, Bethlahem Institute of Engineering, Kanyakumari, Tamil Nadu, India
Abstract
World Health Organization has estimated that four out of five cardiovascular diseases (CVD) deaths are due to heart attacks. Heart disease is an uncommon condition of the heart and the blood circulation. Heart disease is also known as cardiovascular disease which is our country’s main executioner. From past two decades Heart-disease remained as a leading cause of death at global level. Statistics illustrate the lethality of cardiovascular disease by showing the percentage of deaths caused by heart attacks worldwide. Therefore, it is crucial to predict the condition as earliest as possible time. Cardiologist have limitations, they cannot predict heart disease risk to a high degree of accuracy. So, a reliable, accurate and feasible system is required to predict such diseases in time for proper treatment. In order to automate analysis of large and complex medical datasets, Machine Learning algorithms and techniques have been applied. We have also seen machine learning (ML) techniques being used in recent developments in different areas of Internet of Things (IoT). Machine learning has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. The main theme of the paper is the prediction of heart diseases using machine learning techniques by summarizing the few current researches. The main goal of our project is logistic regression algorithms used and the health care data which classifies the patients whether they are having heart diseases or not, according to the information of recorded data. Logistic Regression is a statistical and machine-learning technique classifying records of a dataset based on the values of the input fields. It predicts a dependent variable based on one or more set of independent variables to predict outcomes. It can be used both for binary classification and multi-class classification. Try to use this data a model which predicts the patient whether they are having heart disease or not.
Keywords: Classification, Heart Disease, Decision Tree,Data Mining,
Journal Name :
EPRA International Journal of Multidisciplinary Research (IJMR)

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Published on : 2023-03-09

Vol : 9
Issue : 3
Month : March
Year : 2023
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