Heart Disease Prediction using Hybrid Machine Learning

SHAKIR ALI; Mohammad Saifuddin; Pankaj Mishra

doi:10.36948/ijfmr.2023.v05i04.4705

Heart Disease Prediction using Hybrid Machine Learning

Author(s)	SHAKIR ALI, Mohammad Saifuddin, Pankaj Mishra
Country	India
Abstract	heart disease usually refers to conditions such as narrowed or blocked blood vessels leading to heart failure, pain due to decreased blood flow to the heart (angina), or stroke. Heart disease is one of the leading causes of mortality in the world today. It contributes to about 30% of all deaths worldwide. Predicting cardiovascular disease is a critical challenge in the field of clinical data analytics. Machine learning (ML) has been shown to be effective in making decisions and predictions from the large amount of data generated in the healthcare industry. Machine learning is being used in many fields around the world. The healthcare industry is not exempt. Machine learning can play a significant role in predicting musculoskeletal conditions, heart disease and more. Such information, when predicted in a timely manner, can provide physicians with important clues so they can tailor their diagnosis and treatment to each patient. We are working on predicting potential heart disease in humans using machine learning algorithms. In this work, we present a novel method for identifying important variables that improve the precision of “cardiovascular disease” prediction. Along with a number of feature combinations and well-known classification techniques, the "prediction" model is introduced. When preprocessing the data in an efficient way we can achieve better performance. Preprocessing means handling missing values, removing outliers, balancing the data, scaling the feature, and optimizing the hyperparameter for the tree, selecting the important feature using the feature score technique. Experiment conducted shows that preprocessing technique helps us to get better performances. The experiment result also shows that hybrid random forest with linear model and feature score (HRFLMFS) prediction model for heart disease gives us a better result.
Keywords	Cleveland Heart Disease Database, Decision Trees, Random Forest, Hybrid algorithm, Machine learning
Field	Engineering
Published In	Volume 5, Issue 4, July-August 2023
Published On	2023-07-29
Cite This	Heart Disease Prediction using Hybrid Machine Learning - SHAKIR ALI, Mohammad Saifuddin, Pankaj Mishra - IJFMR Volume 5, Issue 4, July-August 2023. DOI 10.36948/ijfmr.2023.v05i04.4705
DOI	https://doi.org/10.36948/ijfmr.2023.v05i04.4705
Short DOI	https://doi.org/gsjjqs

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International Journal For Multidisciplinary Research

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Heart Disease Prediction using Hybrid Machine Learning

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