International Journal For Multidisciplinary Research

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A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Predictive Modeling of Heart Disease Using Python and Machine Learning

Author(s) Abhishek Upadhyay
Country India
Abstract Cardiovascular disease is a sickness that can cause sudden death. It happens when the heart is not working properly due to many things. There are many factors that can affect the heart, such as obesity, high blood pressure, and cholesterol. The number of cases for death due to heart disease has been increased and there is a need for methods to help predict the disease, aid in early diagnosis, and help doc- tors treat patients medically. The current study aims to estimate the risk of heart attack based on data from patients. In practice, prediction and interpretation are the main goals of data discovery. Predictive data mining involves attributes or variables in datasets to determine unknown or future values of other factors. This definition refers to finding patterns that interpret data for human interpretation. Machine learning is now used in many fields, and healthcare is no exception. K- nearest, random forests etc. such as machine learning algorithms (classification algorithms). Medical care is about people’s lives and should be the right one. Therefore, we need to create a system that can accurately predict the disease. To give treatment for heart disease, a lot of advanced technologies are used. In medical center it is the most common problem that many of medical persons do not have equal knowledge and expertise to treat their patient so they deduce their own decision and as a result it show poor outcome and sometime leads to death. To overcome these problems predictions of heart disease using machine learning algorithms and data mining techniques, it become easy to automatic diagnosis in hospitals as they are playing vital role in this regard. Heart disease can be predicted by performing analysis on patient’s different health parameters. There are different algorithm to predict heart disease like na¨ıve Bayes, k Nearest Neighbor (KNN), Decision tree ,Artificial Neural Network(ANN).We have used different parameters to predict heart disease. Those parameters are Age, Gen- der, Cerebral palsey (CP), Gender, Cerebral palsey (CP), Blood Pressure (bp), Fasting blood sugar test (fbs) etc. In our research paper, we used built in dataset
.we have implement the five different techniques with same dataset to predict heart disease These implemented algorithm are Naive Bayes, k Nearest Neighbor (KNN), Decision tree, Artificial Neural Network (ANN), Random Forest .This paper investigates that which technique gives more accuracy in predicting heart disease based on health parameters. Experiment show that Na¨ıve Bayes has the highest accuracy of 88
Keywords Keywords: Naive Bayes, k Nearest Neighbor (KNN), Decision tree, Artificial Neural Network (ANN), Random Forest, Heart Disease
Field Engineering
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-25
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.49208
Short DOI https://doi.org/g9rskk

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