Heart Disease Prediction Using Novel Quine McCluskey Binary Classifier (QMBC)

KAPILA, Ramdas, RAGUNATHAN, Thirumalaisamy, SALETI, Sumalatha, TANGIRALA, Jaya Lakshmi and AHMAD, Mohd Wazih (2023). Heart Disease Prediction Using Novel Quine McCluskey Binary Classifier (QMBC). IEEE Access, 11, 64324-64347.

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Official URL: http://dx.doi.org/10.1109/access.2023.3289584
Open Access URL: https://ieeexplore.ieee.org/document/10163792 (Published version)
Link to published version:: https://doi.org/10.1109/access.2023.3289584


Cardiovascular disease is the primary reason for mortality worldwide, responsible for around a third of all deaths. To assist medical professionals in quickly identifying and diagnosing patients, numerous machine learning and data mining techniques are utilized to predict the disease. Many researchers have developed various models to boost the efficiency of these predictions. Feature selection and extraction techniques are utilized to remove unnecessary features from the dataset, thereby reducing computation time and increasing the efficiency of the models. In this study, we introduce a new ensemble Quine McCluskey Binary Classifier (QMBC) technique for identifying patients diagnosed with some form of heart disease and those who are not diagnosed. The QMBC model utilizes an ensemble of seven models, including logistic regression, decision tree, random forest, K-nearest neighbour, naive bayes, support vector machine, and multilayer perceptron, and performs exceptionally well on binary class datasets. We employ feature selection and feature extraction techniques to accelerate the prediction process. We utilize Chi-Square and ANOVA approaches to identify the top 10 features and create a subset of the dataset. We then apply Principal Component Analysis to the subset to identify 9 prime components. We utilize an ensemble of all seven models and the Quine McCluskey technique to obtain the Minimum Boolean expression for the target feature. The results of the seven models ( x_0, x_1, x_2,⋖, x_6 ) are considered independent features, while the target attribute is dependent. We combine the projected outcomes of the seven ML models and the target feature to form a foaming dataset. We apply the ensemble model to the dataset, utilizing the Quine McCluskey minimum Boolean equation built with an 80:20 train-to-test ratio. Our proposed QMBC model surpasses all current state-of-the-art models and previously suggested methods put forward by various researchers.

Item Type: Article
Uncontrolled Keywords: 08 Information and Computing Sciences; 09 Engineering; 10 Technology; 40 Engineering; 46 Information and computing sciences
Identification Number: https://doi.org/10.1109/access.2023.3289584
Page Range: 64324-64347
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 27 Feb 2024 14:21
Last Modified: 01 Mar 2024 08:00
URI: https://shura.shu.ac.uk/id/eprint/33297

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