A Model to Detect Heart Disease using Machine Learning Algorithm

Taylor, O. E.; Ezekiel, P. S.; Okuchaba, F.B. Deedam

doi:10.26438/ijcse/v7i11.15

Cited by 16 publications

(4 citation statements)

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“…Our study yields the most encouraging results for heart failure prediction, especially by adding certain feature selection algorithms [25]. ANN+SVM 99% [19] Ensemble Techniques 90% [20] Gaussian Naive Bayes+SVM+ RF+KNN +XGBoost 88.52% [21] ANN + Logistic Regression 85% [22] Hybrid CNN-GRU 94% [23] Decision Tree 98% [24] Decision Tree 98% Proposed Decision Tree with prominent feature selection Accuracy and all metrics Cross-validation 100% 99.51%…”

Section: Comparison With State-of-the-art Studiesmentioning

confidence: 92%

Clinical Support System for Cardiovascular Disease Forecasting Using ECG

Ahmed,

Naz,

Agarwal

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Heart failure is a chronic condition that affects many people worldwide. Regrettably, it is now the biggest cause of mortality globally, and it is becoming more common. Before a cardiac event, early diagnosis of heart disease is challenging. Although healthcare institutions like hospitals and clinics have access to a wealth of heart disease data, it is rarely used to uncover underlying trends. OBJECTIVES: Algorithms for machine learning (ML) can turn this medical data into insightful information. These methods are used to create decision support systems (DSS) that can gain knowledge from the past and advance. It is essential to use an effective ML-based technique to identify early heart failure and take preventive action to address this worldwide issue. Accurately identifying heart illness is our main goal in this study. METHODS: For this work, we benchmark different datasets on heart illness, and we use feature engineering approaches to pick the most pertinent qualities for improved performance. Additionally, we assess nine ML methods using critical parameters including precision, f-measure, sensitivity, specificity, and accuracy. RESULTS: Iterative tests are carried out to evaluate the efficacy of different algorithms. With a flawless cross-validation accuracy score of 99.51% and 100% in all other metrics, our suggested Decision Tree approach performs better than other ML models and cutting-edge studies. CONCLUSION: Each methodology used in our study is validated using cross-validation techniques. The medical community benefits greatly from this research study.

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Section: Comparison With State-of-the-art Studiesmentioning

confidence: 92%

Clinical Support System for Cardiovascular Disease Forecasting Using ECG

Ahmed,

Naz,

Agarwal

et al. 2024

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

show abstract

“…Numerous ML and DL techniques have been applied to disease prediction systems in the medical field. Gradient descent optimization [12], Deep neural networks [13], bagging ensemble methods [14], XGBoost [15], J48 [16], Random Forest [17], and Decision tree [18] were widely used in the classification of disease. A hybrid approach was created by Mohan et al, [19] that demonstrated an innovative way to extract necessary properties from data to understand and classify according to the vital patterns by using ML classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…Using high entropy inputs, trees are constructed for training samples of data D. In this simple and fast trees are constructed using a top-down recursive divide and conquer strategy. As part of the tree-pruning process, irrelevant samples are removed from D [18].…”

Section: ) Decision Treementioning

confidence: 99%

An Integrated Two-Layered Voting (TLV) Framework for Coronary Artery Disease Prediction Using Machine Learning Classifiers

Omkari,

Shaik

2024

IEEE Access

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Cardiovascular problems have emerged as a significant concern, adversely impacting individuals across all age groups. Several recent research studies have used Machine learning (ML) techniques to design decision-making systems for the tremendous data in the medical sector. Although these works obtained promising results, most of the studies focused on small datasets. Since the size of the dataset affects algorithm performance, this study used two datasets, such as Kaggle's heart disease dataset of over 70,000 records and UCI's heart disease dataset of 1025 records. In addition to the old features the Pulse Pressure (PP), the Body Mass Index (BMI), and the Mean Arterial Pressure (MAP), three new features are introduced to improve the results. This paper proposes the TLV (Two-Layer Voting) model, which is an ensemble method of hard and soft voting. As part of layer 1, features are shortlisted by soft and hard voting using three statistical methods, including the ANOVA f-test, Chi-squared test, and Mutual Information. In layer 2, soft voting and hard voting performance are compared, which incorporates Multi-Layer Perceptron, Decision Tree, Support Vector Classifier, and Random Forest algorithms. Classification algorithms are hyper-tuned using the GridSearchCV method in the second layer. Using UCI's heart disease dataset and Kaggle's CVD dataset, the proposed TLV methodology with soft voting provided the highest accuracy of 99.03% and 88.09%, respectively. The proposed model significantly outperforms existing CAD disease prediction studies.

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“…Heart disease also refers to conditions defined by narrowed or blocked blood vessels that can result in a cardiac arrest, chest pain, or pulmonary embolism. This study [8] presents a machine learning algorithmbased model for detecting heart disease. The Agile Model was used in this research, which includes parallel planning, requirements analysis, developing, programming, screening, and paperwork throughout the process of production.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Efficient Machine Learning Model to Detect Muliebrous

Nagar¹,

Jain²,

Shukla³

2023

Artificial Intelligence and Communication Technologies

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The Diabetes predicting machine learning models developed so far Diabetes risk calculators have a strong negative predictive value, which means they can help identify people who are unlikely to develop diabetes. Tests that reflect beta cell function may assist determine the type of diabetes or the requirement for insulin indefinitely. Machine Learning models can help predict whether or not a female is suffering from diabetes while assessing different health conditions such as; increased glucose level, age, family hereditary and blood pressure. Our project focuses on making machine learning models which can predict if a person suffers from diabetes while using specific health conditions as constraints. So different machine learning models will be used in order to get concluding results and whichever model serves to be most accurate will be used to improve the prediction system of diabetes amongst female patients. The project uses several concepts of machine learning, and the data used are databases obtained from national authorities to test and verify the results. Diabetes, if ignored, can cause various life threatening situations for the people suffering from it and hence we aim to help predict diabetes with accuracy among the female population.

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A Model to Detect Heart Disease using Machine Learning Algorithm

Cited by 16 publications

References 0 publications

Clinical Support System for Cardiovascular Disease Forecasting Using ECG

Clinical Support System for Cardiovascular Disease Forecasting Using ECG

An Integrated Two-Layered Voting (TLV) Framework for Coronary Artery Disease Prediction Using Machine Learning Classifiers

Development of Efficient Machine Learning Model to Detect Muliebrous

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