Early Prediction of Stroke Disease Diagnosis Patients Using Data Mining Algorithm Comparison

Pungkas Subarkah; Wenti Risma Damayanti; Arbangi Puput Sabaniyah

doi:10.24014/ijaidm.v7i1.25955

Early Prediction of Stroke Disease Diagnosis Patients Using Data Mining Algorithm Comparison

Pungkas Subarkah, Wenti Risma Damayanti, Arbangi Puput Sabaniyah

Abstract

Stroke constitutes a medical emergency of paramount significance, characterized by a notably elevated mortality rate, and stands as the foremost cause of mortality within hospital settings. The dataset employed for this analysis is sourced from Kaggle, denoted as the Brain Stroke Dataset, encompassing a total of 4981 records. This research aims to carry out early prediction of stroke sufferers using several algorithms including the ANN algorithm, CART, KNN, and the NBC algorithm. The results obtained in the ANN algorithm obtained an accuracy of 93.53%, in the CART algorithm of 95.02%, in the KNN algorithm of 91.09% and in the NBC algorithm of 88.44%. With the outcomes of this research, the use of the cart set of rules may be used for early evaluation of stroke sufferers because it has a good degree of accuracy and is listed inside the excellent type kind

Keywords

Early Prediction; Stroke; Diagnosis; Comparison Algorithm

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References

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DOI: http://dx.doi.org/10.24014/ijaidm.v7i1.25955

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