Enhanced cardiovascular disease prediction model using random forest algorithm

Sumwiza, Kellen; Twizere, Celestin; Rushingabigwi, Gerard; Bakunzibake, Pierre; Bamurigire, Peace

doi:10.1016/j.imu.2023.101316

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…The proposed model yields the 83% of accuracy. (Sumwiza, et al, 2023) used the outlier removal in their research paper Enhanced cardiovascular disease prediction model using random forest algorithm. Correlation coefficient and data mining feature selection techniques are applied to remove the outlier.…”

Section: Related Workmentioning

confidence: 99%

Heart Disease Prediction Using Outlier Removal based Max Voting Ensemble Method

Chapagain

2023

Int. J. Engin. Technol.

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Heart disease has emerged as a serious health concern for many individuals due to its high death rate around the world. The routine clinical data analysis has a significant difficulty in the early diagnosis of cardiac disease. The identification of cardiac disease may benefit from the use of machine learning. To improve machine learning models, several studies have previously been conducted. The suggested study uses the maximum voting ensemble technique of classification to effectively identify heart disease. The suggested classifier is a more reliable and accurate approach. To identify and eliminate outliers, conduct Inter quartile range outlier removal and min-max normalization during preprocessing. Accuracy, Precision, Recall, and F1 Score are calculated and evaluated against various models. For the heart disease dataset collected from the Kaggle, the suggested max voting ensemble classifier has an accuracy of 99.22%.

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Section: Related Workmentioning

confidence: 99%