Heart Disease Detection Using ML

Das, Ranjit Chandra; Das, Madhab Chandra; Hossain, Md. Amzad; Rahman, Mohammad Abdur; Hossen, Md Helal; Hasan, Rakibul

doi:10.1109/ccwc57344.2023.10099294

Cited by 14 publications

(1 citation statement)

References 29 publications

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“…The prevention of most cardiovascular diseases can be achieved by tackling behavioral risk factors, which include inadequate physical activity, tobacco consumption, an unhealthy diet leading to obesity, and excessive alcohol consumption. Due to these reasons, young people have started facing issues like obesity, high cholesterol, and blood pressure levels resulting in premature heart failure and death [2]. Therefore, it is crucial to detect the early symptoms of CVD to provide early medication to prevent casualties.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Cardiovascular Disease with Different Machine Learning Approaches

Singh,

Rai

et al. 2024

EAI Endorsed Trans IoT

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With the increase in mortality rate around the world in recent years, cardiovascular diseases (CVD) have swiftly become a leading cause of morbidity, and therefore there arises a need for early diagnosis of disease to ensure effective treatment. With machine learning emerging as a promising tool for the detection, this study aims to propose and compare various algorithms for the detection of CVD via several evaluation metrics including accuracy, precision, F1 score, and recall. ML has the ability and potential to improve CVD prediction, detection, and treatment by analysis of patient information and identification of patterns that may be difficult for humans to interpret and detect. Several state-of-the-art ML and DL models such as Decision Tree, XGBoost, KNN, and ANN were employed. The results of these models reflect the potential of Machine Learning in the detection of CVD detection and subsequently highlight the need for their integration into clinical practice along with the suggestion of the development of robust and accurate models to improve the predictions. This integration, however, significantly helps in the reduction of the burden of CVD on healthcare systems.

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Section: Introductionmentioning

confidence: 99%

Early Detection of Cardiovascular Disease with Different Machine Learning Approaches

Singh,

Rai

et al. 2024

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

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Applying data science approach to predicting diseases and recommending drugs in healthcare using machine learning models – A cardio disease case study

Lambay,

Mohideen

2024

Multimed Tools Appl

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Heart Disease Prediction Using Stacking Model With Balancing Techniques and Dimensionality Reduction

Noor,

Javaid,

Alrajeh

et al. 2023

IEEE Access

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Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart disease is one of the leading causes of mortality worldwide, early detection is crucial. Emerging technologies like Machine Learning (ML) are currently being actively used by the biomedical, healthcare, and health prediction industries. PaRSEL, a new stacking model is proposed in this research, that combines four classifiers, Passive Aggressive Classifier (PAC), Ridge Classifier (RC), Stochastic Gradient Descent Classifier (SGDC), and eXtreme Gradient Boosting (XGBoost), at the base layer, and LogitBoost is deployed for the final predictions at the meta layer. The imbalanced and irrelevant features in the data increase the complexity of the classification models. The dimensionality reduction and data balancing approaches are considered very important for lowering costs and increasing the accuracy of the model. In PaRSEL, three dimensionality reduction techniques, Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and Factor Analysis (FA), are used to reduce the dimensionality and select the most relevant features for the diagnosis of heart disease. Furthermore, eight balancing techniques, Proximity Weighted Random Affine Shadowsampling (ProWRAS), Localized Randomized Affine Shadowsampling (LoRAS), Random Over Sampling (ROS), Adaptive Synthetic (ADASYN), Synthetic Minority Oversampling Technique (SMOTE), Borderline SMOTE (B-SMOTE), Majority Weighted Minority Oversampling Technique (MWMOTE) and Random Walk Oversampling (RWOS), are used to deal with the imbalanced nature of the dataset. The performance of PaRSEL is compared with the other standalone classifiers using different performance measures like accuracy, F1-score, precision, recall and AUC-ROC score. Our proposed model achieves 97% accuracy, 80% F1-score, precision is greater than 90%, 67% recall, and 98% AUC-ROC score. This shows that PaRSEL outperforms other standalone classifiers in terms of heart disease prediction. Additionally, we deploy SHapley Additive exPlanations (SHAP) on our proposed model. It helps to understand the internal working of the model. It illustrates how much influence a classifier has on the final prediction outcome.

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Heart Disease Detection Using ML

Cited by 14 publications

References 29 publications

Early Detection of Cardiovascular Disease with Different Machine Learning Approaches

Early Detection of Cardiovascular Disease with Different Machine Learning Approaches

Applying data science approach to predicting diseases and recommending drugs in healthcare using machine learning models – A cardio disease case study

Heart Disease Prediction Using Stacking Model With Balancing Techniques and Dimensionality Reduction

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