Comparative Analysis of Machine Learning Methods for Prediction of Heart Diseases

Stepanyan, Ivan V.; Alimbayev, Ch. A.; Savkin, M. O.; Lyu, D.; Zidun, M.

doi:10.3103/s1052618822080210

Cited by 3 publications

(6 citation statements)

References 22 publications

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“…The logistic regression exhibits resistance to outliers Cardiovascular Decision Tree [15], [127], [146], [157], [212] max_depth: none, criterion: gini Explaining the relationship between predictor variables and the target Random Forest [15], [127], [132], [146], [157], [212], [213] Criterion: gini, n_estimators: 100…”

Section: Disease Machine Learning Algorithm Parameter Used Reasons Th...mentioning

confidence: 99%

“…XGBoost has various parameters that can be optimized to enhance the models performance, such as the learning rate NN [15] hidden layers: 100, activation: truncated linear transform, optimization: adam, epoch: 200…”

Section: Disease Machine Learning Algorithm Parameter Used Reasons Th...mentioning

confidence: 99%

“…Effectively handling the presence of missing values in the data KNN [15], [146], [166], [212], [214] metric: minkowski distance, n_neighbor: 5, weight: distance The strength to handle outliers is due to its noninvolvement in assumptions about a specific data distribution SVM [15], [16], [146], [157] Kernel: rbf, degree of polynomial: 3, cache size: 200mb…”

Section: Disease Machine Learning Algorithm Parameter Used Reasons Th...mentioning

confidence: 99%

“…Effective in handling datasets with a smaller number of samples Logistic Regression [15], [16], [146], [157], [212], [213] C: 100, optimization algorithm: broyden-Fletcher-Goldfarb-Shanno algorithm with limited memory, penalty: ridge…”

Section: Disease Machine Learning Algorithm Parameter Used Reasons Th...mentioning

confidence: 99%

“…Machine learning can be used for data mining in the healthcare sector [4]. Applying machine learning in health data can help predict if a patient might have six chronic diseases: diabetes mellitus [5], [6]; cancer [7], [8]; stroke [9], [10]; hypertension [11], [12]; kidney failure [13], [14]; and heart issues [15], [16].…”

Section: Introductionmentioning

confidence: 99%

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Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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According to the World Health Organization (WHO), some chronic diseases such as diabetes mellitus, stroke, cancer, cardiac vascular, kidney failure, and hypertension are essential for early prevention. One of the prevention that can be taken is to predict chronic diseases using machine learning based on personal medical record or general checkup result. The common prediction objective is to minimize the prediction error as low as possible. The most influencing chronic diseases prediction factors are the quality of data and the choice of predictor such as machine learning methods. The five main problems those lower data quality are outliers, missing values, feature selection, normalization, and imbalance. After we ensure the quality of data, the next task is to choose the best machine learning methods. The most influencing factor to consider when we choose the predictor its performance evaluation (accuracy, recall, precision, f1-score). Thus, predicting chronic disease aims to produce increased performance and solve problems in medical data. This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. This paper covers machine learning methods discussion such as supervised learning, ensemble learning, deep learning, and reinforcement learning. The preprocessing handling we discuss includes missing values, outliers, feature selection, normalization, and imbalance. The final discussions of this paper are open issues, and the potential future works in improving the prediction performance for chronic diseases using a data preprocessing handling and machine learning methods.

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Section: Disease Machine Learning Algorithm Parameter Used Reasons Th...mentioning

confidence: 99%

Section: Disease Machine Learning Algorithm Parameter Used Reasons Th...mentioning

confidence: 99%

Section: Disease Machine Learning Algorithm Parameter Used Reasons Th...mentioning

confidence: 99%

Section: Disease Machine Learning Algorithm Parameter Used Reasons Th...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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IoT-Based Intelligent Medical Decision Support System for Cardiovascular Diseases

Menaceur,

Kouah,

Derdour

2023

Lecture Notes in Networks and Systems

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Artificial Intelligence based Early Detection of Cardiovascular Diseases

Prakash,

Krishnaleela,

Mahendran

et al. 2024

2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN)

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Comparative Analysis of Machine Learning Methods for Prediction of Heart Diseases

Cited by 3 publications

References 22 publications

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

IoT-Based Intelligent Medical Decision Support System for Cardiovascular Diseases

Artificial Intelligence based Early Detection of Cardiovascular Diseases

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