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A Hybrid Predictive Model to Detect Heart Attack Possibility Using Artificial Intelligence Algorithms
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
This study was able to further x-ray the power of artificial intelligence once more in providing a leading and more accurate knowledge that could help detect early enough disease progression on time. Heart attack prediction using 299 dataset involving clinical data from two different health sectors in Owerri Municipal council by using a Machine learning software development life circle (ML-SDLC) methodological approach which produced a hybrid model to detect high accuracy on the predicted dataset shows that Random Forest (RF) with validation accuracy of 0.875 =88%, test accuracy 0.881=88% and Out-of-bag accuracy 0.614 = 61% while K-Nearest Neighbors (KNN) with validation accuracy of 0.729 = 73% and test accuracy 0.746 = 75% respectively has attributed death event witnessed in this areas as caused by delay in time for detection of the disease. Evaluation Metrics of all factors, confusion matrices, Roc Curves, Andrews curve, Precision (positive predictive value)/support and the RF out-of-bag result shown in Table 3 and 6 of this paper was able to develop a high percentage rate evaluation result on the two applied algorithms that helped in the development of the heart attack predictive model for disease detection and faster decision making.
Keywords
Artificial Intelligence, Machine Learning, Random Forest and K-Nearest Neighbors (KNN) Classification Models, Heart Attack Prediction Early detection of heart disease
References
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[8] Anjan Nikhil Repaka, Sai Deepak Ravikanti and Ramya G Franklin, (2017) "Design And Implementing Heart Disease Prediction Using Naives Bayesian," 2019 3rd
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How to cite this paper
@article{1708066,
author = {Oguoma Ikechukwu Stanley, Uka Kanayo Kizito, Chukwu Alphonsus Chekwube, Ikechukwu Peter Amujiogu, David Osondu Akuchie; Shanice Kristy Archibald},
title = {A Hybrid Predictive Model to Detect Heart Attack Possibility Using Artificial Intelligence Algorithms},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {10},
pages = {1001-1009},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708066.pdf},
abstract = {This study was able to further x-ray the power of artificial intelligence once more in providing a leading and more accurate knowledge that could help detect early enough disease progression on time. Heart attack prediction using 299 dataset involving clinical data from two different health sectors in Owerri Municipal council by using a Machine learning software development life circle (ML-SDLC) methodological approach which produced a hybrid model to detect high accuracy on the predicted dataset shows that Random Forest (RF) with validation accuracy of 0.875 =88%, test accuracy 0.881=88% and Out-of-bag accuracy 0.614 = 61% while K-Nearest Neighbors (KNN) with validation accuracy of 0.729 = 73% and test accuracy 0.746 = 75% respectively has attributed death event witnessed in this areas as caused by delay in time for detection of the disease. Evaluation Metrics of all factors, confusion matrices, Roc Curves, Andrews curve, Precision (positive predictive value)/support and the RF out-of-bag result shown in Table 3 and 6 of this paper was able to develop a high percentage rate evaluation result on the two applied algorithms that helped in the development of the heart attack predictive model for disease detection and faster decision making.},
keywords = {Artificial Intelligence, Machine Learning, Random Forest and K-Nearest Neighbors (KNN) Classification Models, Heart Attack Prediction Early detection of heart disease},
month = {April},
}