Machine Learning-Based Cardiovascular Disease Detection Using Optimal Feature Selection

Ullah, Tahseen; Ullah, Syed Irfan; Ullah, Khalil; Ishaq, Muhammad; Khan, Ahmad; Ghadi, Yazeed Yasin; Algarni, Abdulmohsen

doi:10.1109/access.2024.3359910

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Lack of breathing, chest discomfort, fast or rapid heartbeats, and fatigue are the primary signs and symptoms of heart disease [2]. In the USA (US), the prevalence of cardiovascular illness is significantly higher, while one adult passes away from heart diseases each thirty-four seconds [3]. Roughly 25 million individuals worldwide suffer from cardiovascular conditions [4].…”

Section: Introductionmentioning

confidence: 99%

Detect the Cardiovascular Disease's in Initial Phase using a Range of Feature Selection Techniques of ML

Goad,

J Deore

2024

Int. Res. J. multidiscip. Technovation

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Heart-related conditions remain the foremost global cause of mortality. In 2000, heart disease claimed around 14 million lives worldwide, a number that surged to approximately 620 million by 2023. The aging and expanding population significantly contribute to this rising mortality trend. However, this also underscores the potential for significant impact through early intervention, crucial for reducing fatalities from heart failure, where prevention plays a pivotal role. The aim of the present research is to develop a prospective ML framework that can detect important features and predict cardiac conditions as an early stage using a variety of choice of features strategies. The Features subsets that were chosen were designated as FST1, FST2, and FST3, respectively. Three distinct methods, including correlation-based feature selection, chi-square and mutual information, were used for picking features. Next, the most confident theory & the most appropriate feature selection were identified using six alternative machine learning models: Logistical Regression (LR) (AL1), the support vector Machine (SVM ) (AL2), K-nearest neighbor (K-NN) (AL3), Random forest (RF) model (AL4), Naive Bayes (NB) model (AL5), and Decision Tree (DT) (AL6). Ultimately, we discovered that, with 95.25% accuracy, 95.11% sensitivity, 95.23% specificity, 96.96 area below receiver operating characteristic and 0.27 log loss, the random forest model offered the most excellent results for F3 feature sets. No one has investigated coronary artery disease forecasting in depth; however, our study evaluates multiple statistics (specificity, sensitivity, accuracy, AUROC, and log loss) and uses multiple attribute choices to improve algorithms success for important features. The suggested model has considerable promise for medical use to speculate CVD find in Precursor at a minimal cost and in a shorter amount of time as well as will assist limited experience physician to take right decision based on the results of the used model combined with specific criteria.

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Section: Introductionmentioning

confidence: 99%

Detect the Cardiovascular Disease's in Initial Phase using a Range of Feature Selection Techniques of ML

Goad,

J Deore

2024

Int. Res. J. multidiscip. Technovation

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Comprehensive Review of Machine Learning Applications in Heart Disease Prediction

Kumar,

Kiran Kaur,

Singh

2024

International Journal of Innovative Science and Research Techno

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Heart infections are responsible, for deaths and nowthey are a major contributor to depression in many individuals. To prevent fatalities, regular monitoring and early identification of heart conditions can significantly reduce the number of deaths.Detecting heart disease has become a task in the analysis ofdata. While accurately predicting heart infections may pose challenges employing advanced machine learning techniques can make it easier. Studies have shown that machine learning methods can effectively predict heart disease enabling detection and assessment of its severity. This approach aims to lower mortality rates decrease the severity of the illness and facilitate diagnosis. The field of therapy is undergoing advancements through the integration of machine learning techniques leading to enhanced accuracy in interpreting analyses. These techniques play a role,in identifying indicators for predicting cardiac diseases with precision. The presentation is put together using categorization techniques, such, as Decision Tree (DT) K Nearest Neighbors(K NN) Random Forest (RF) and Support Vector Machine (SVM). The performance of these four algorithms is assessedfrom angles, including specificity, recall, accuracy and precision. While precision varies SVM appears to deliver the results in this approach for calculations, in many instances.

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Machine Learning-Based Cardiovascular Disease Detection Using Optimal Feature Selection

Cited by 2 publications

References 34 publications

Detect the Cardiovascular Disease's in Initial Phase using a Range of Feature Selection Techniques of ML

Detect the Cardiovascular Disease's in Initial Phase using a Range of Feature Selection Techniques of ML

Comprehensive Review of Machine Learning Applications in Heart Disease Prediction

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