Prediction of Heart Disease Using Naive Bayes in Comparison with KNN Based on Accuracy

Thummala, Gunasekhar Reddy; Baskar, Radhika; Thiyaneswaran, N

doi:10.1109/iccr56254.2022.9995841

Cited by 3 publications

(4 citation statements)

References 40 publications

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“…Thummala Gunasekhar, et al [166] compared the KNN and Naïve Bayes methods to determine their effectiveness for heart disease prediction. The Naïve Bayes method outperformed the KNN method with a superiority of 3% in accuracy.…”

Section: G Knnmentioning

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%

“…The logistic regression exhibits resistance to outliers Naive Bayes [15], [146], [166] var_smoothing: 10 9…”

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

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: G Knnmentioning

confidence: 99%

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

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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“…There is a lot of criteria performance that was conducted in comparing performance between methods in Machine Learning or Deep Learning. The comparison performance method has conducted based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percent Error (MAPE) [8], Quality Metrics and Execution Time [9], Coefficient of Determination values and Mean Absolute Error values [12], Accuracy Value [14], [16], precision, recall, ROC Curve and PRC [15], [17], Mean Absolute Error (MAE) and Mean Square Error (MSE) [18], Cross Validation [19], Root Mean Squared Error [20], Mean Accuracy [21], Internal and External Verification [22], Accuracy [23], Accuracy, Sensitivity, dan Specificity [24], and Precision, Recall, f1 Score, RMSE, Kappa Coefficient, Matthew Correlation Coefficient, Receiver Operating Characteristics (ROC) Curve and Accuracy [25]. Based on a lot of the previous research, this study is a predictive measurement of ongoing events for patients who survive and is national by using the best algorithm from several algorithms.…”

Section: A Literature Reviewmentioning

confidence: 99%

The Effect of The Prediction of The K-Nearest Neighbor Algorithm on Surviving COVID-19 Patients in Indonesia

Martono,

Henderi,

Maulani

2023

Ilk. J. Ilm.

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This study aims to measure the prediction of survival of covid-19 patients with the best algorithm based on RMSE(Root Mean Square Error). The Covid-19 pandemic has lasted from December 2019 until now and is full of uncertainty about when this pandemic will end, so this research was carried out. In this study, the knowledge discovery database method was used by extracting data sets from Covid-19 patients from March 2020 to March 2021 for each province in Indonesia (Dataset from Kawal Covid-19 SintaRistekbrin) to predict survival during this pandemic as measured by the best algorithms include k-NN (k-Nearest Neighbor), SVM (Support Vector Machine), and/or Deep Learning. The measurement results using cross-validation and the optimal number of folds is 3 in the form of RSME, showing that the k-NN algorithm is an algorithm with RSME 0.101 +/-0.23 where the error rate is the lowest compared to the two algorithms above. Therefore, the k-NN algorithm was chosen as the algorithm for the predictive measurement of surviving Covid-19 patients.

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Effective Heart Attack Prediction method using Machine Learning Algorithm

Chitra,

Govindaraj

2024

2024 10th International Conference on Communication and Signal Processing (ICCSP)

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Prediction of Heart Disease Using Naive Bayes in Comparison with KNN Based on Accuracy

Cited by 3 publications

References 40 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

The Effect of The Prediction of The K-Nearest Neighbor Algorithm on Surviving COVID-19 Patients in Indonesia

Effective Heart Attack Prediction method using Machine Learning Algorithm

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