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

Kapila, Ramdas; Ragunathan, T.; Saleti, Sumalatha; Lakshmi, T. Jaya; Ahmad, Mohd Wazih

doi:10.1109/access.2023.3289584

Cited by 14 publications

(11 citation statements)

References 45 publications

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“…RF is effective in both regression and classification tasks. The regression averages the outputs from different trees to make prediction at new point x, as shown in Equation ( 3) [40].…”

Section: Machine-learning (Ml) Modelsmentioning

confidence: 99%

“…In Equation (6), there are two possible events: A and B [27]. The NB classifier calculates the posterior probability P( C i |X) of each class C i based on the input features and predicts the class with the highest probability [40] using the formula given below:…”

Section: Machine-learning (Ml) Modelsmentioning

confidence: 99%

“…KNN classifies new input data based on their distance from data already classified by K-nearest neighbor [19]. The value of k in a KNN indicates how many nearest neighbors were utilized to create predictions for the input test sample [40]. The selection of the k-nearest neighbors is conducted based on having the smallest distances to the input test sample.…”

Section: Machine-learning (Ml) Modelsmentioning

confidence: 99%

“…. , C m , the model function selects the most common model value [40]. Such a classifier demonstrates its versatility by not only combining different models but also allowing for both hard and soft voting mechanisms.…”

Section: Machine-learning (Ml) Modelsmentioning

confidence: 99%

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An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection

Korial,

Gorial,

Humaidi

2024

Computers

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Cardiovascular disease (CVD) is a leading cause of death globally; therefore, early detection of CVD is crucial. Many intelligent technologies, including deep learning and machine learning (ML), are being integrated into healthcare systems for disease prediction. This paper uses a voting ensemble ML with chi-square feature selection to detect CVD early. Our approach involved applying multiple ML classifiers, including naïve Bayes, random forest, logistic regression (LR), and k-nearest neighbor. These classifiers were evaluated through metrics including accuracy, specificity, sensitivity, F1-score, confusion matrix, and area under the curve (AUC). We created an ensemble model by combining predictions from the different ML classifiers through a voting mechanism, whose performance was then measured against individual classifiers. Furthermore, we applied chi-square feature selection method to the 303 records across 13 clinical features in the Cleveland cardiac disease dataset to identify the 5 most important features. This approach improved the overall accuracy of our ensemble model and reduced the computational load considerably by more than 50%. Demonstrating superior effectiveness, our voting ensemble model achieved a remarkable accuracy of 92.11%, representing an average improvement of 2.95% over the single highest classifier (LR). These results indicate the ensemble method as a viable and practical approach to improve the accuracy of CVD prediction.

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“…RF is effective in both regression and classification tasks. The regression averages the outputs from different trees to make prediction at new point x, as shown in Equation ( 3) [40].…”

Section: Machine-learning (Ml) Modelsmentioning

confidence: 99%

Section: Machine-learning (Ml) Modelsmentioning

confidence: 99%

Section: Machine-learning (Ml) Modelsmentioning

confidence: 99%

Section: Machine-learning (Ml) Modelsmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection

Korial,

Gorial,

Humaidi

2024

Computers

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“…Methods based on clustering analysis can offer a nonparametric, unsupervised way for data analysis. A unique FCM algorithm is presented, adopting the concept of the Nearest Neighbor (NN) rule [16,17]. Missing characteristics in medical profiles are unclear and indicated by NN intervals ba sed on closest neighbour data.…”

mentioning

confidence: 99%

Whale optimization approach for early heart disease prediction based on FCM using DCNN

Aswathi,

Kumar,

Ramakrishnan

2023

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Heart disease is the leading cause of death worldwide. It has an impact on not only the health of patients but also the economies and expenses of the countries. Numerous machine learning and data mining approaches are being developed and explored currently in order to predict various diseases. This paper aims to address the pressing global issue of heart disease by leveraging machine learning and data mining techniques. Specifically, it focuses on utilizing a Fuzzy C means (FCM) approach for attribute segmentation, employing the Whale Optimization Algorithm (WOA) for feature selection, and utilizing Deep Convolutional Neural Networks (DCNNs) for medical diagnosis and early prediction. In this study, the initial stage involves segmenting patient records' attributes using the FCM method. Subsequently, high-ranking features are selected through the WOA algorithm. These segmented features are then input into DCNNs to construct a robust medical diagnosis system and enable early-stage prediction. The DCNNs autonomously extract crucial features without human intervention, enhancing the accuracy of disease prediction. The performance evaluation of the proposed classifier is conducted using the Python platform, with the DCNN achieving an impressive accuracy level of 90.12% during testing. This indicates the DCNN's capability to accurately predict the presence or absence of cardiac disease, showcasing its potential as an effective tool in healthcare. The integration of FCM attribute segmentation, WOA feature selection, and DCNN-based prediction holds significant practical implications. It offers healthcare professionals a valuable tool for diagnosing and predicting heart disease early, potentially saving lives.

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Comparative Analysis of Optimization Algorithms for Feature Selection in Heart Disease Classification

Kapila,

Saleti

2024

Lecture Notes in Networks and Systems

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

Cited by 14 publications

References 45 publications

An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection

An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection

Whale optimization approach for early heart disease prediction based on FCM using DCNN

Comparative Analysis of Optimization Algorithms for Feature Selection in Heart Disease Classification

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