Innovative methods to apply on medical data for predicting Heart disease using Machine Learning approach

DOI

10.2991/978-94-6463-471-6_135How to use a DOI?

Keywords

Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers likeDecision Tree Bagging Method (DTBM)

Abstract

Accurately predicting the onset of cardiovascular disease is one of the most pressing problems in modern medicine. Medical experts devote a great deal of time trying to pin down what's causing this. The goal of using several algorithms, including LR, KNN, SVM, GBC, and the GridSearchCVs, is to forecast cardiac illness. The optimal strategy for hyperparameter testing is to use GridSearchCV in conjunction with the Extreme Gradient Boosting Classifier. We compare these results to those of previous studies that focused on cardiac prediction. For the purposes of feature selection and dimensionality reduction, principal component analysis (PCA) was employed. In order to obtain early prediction of heart disorders using data mining approaches, a number of machine learning classifiers, iterative dichotomization, decision tables, and classification and regression trees were utilised. Combination was performed on the log datasets originating from Long Beach, Stat, Switzerland, Virginia, and Cleveland. When the Relief and LASSO methods are applied, it becomes possible to choose features appropriately. The decision tree bagging technique (DTBM), the RFBM, the KNNBM, the ABCM, and the GBBM are all new hybrid classifiers that borrow training from traditional classifiers. With the help of several machine learning techniques, along with our model's accuracy, sensitivity, error rate, precision, and F1 score, we were able to determine the negative predictive value, false positive rate, and false negative rate. In order to facilitate comparisons, the findings are presented independently.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - V. S. A. Chandramouli
AU  - P. Devabalan
AU  - A. D. Devi
AU  - D. Manoj
AU  - G. R. Kumar
AU  - B. Sandeep
PY  - 2024
DA  - 2024/07/30
TI  - Innovative methods to apply on medical data for predicting Heart disease using Machine Learning approach
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1395
EP  - 1401
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_135
DO  - 10.2991/978-94-6463-471-6_135
ID  - Chandramouli2024
ER  -