PCA and t-SNE Implementation for KNN Hypertension Classification

PCA and t-SNE Implementation for KNN Hypertension Classification

Hypertension;KNN;Dimensionality Reduction;PCA;t-SNE

Hypertension is a condition that, if allowed to increase, can significantly injure internal organs due to high blood pressure. The objective of this study is to use the K-Nearest Neighbor (KNN) algorithm along with PCA and t-SNE to accurately identify four categories of Hypertension,Normal, Hypertension, Stage 1 Hypertension, and Stage 2Hypertension. After establishing the scope, a dataset consistingof 7,794 samples was sourced from Labuang Baji Regional General Hospital, Makassar, and contained age, weight, and systolic and diastolic blood pressure parameters. The class distribution is Normal (36.3%), Hypertension (43.12%), Stage 1 Hypertension (8.29%), and Stage 2 Hypertension (12.31%). Experimental results show that the KNN base model achieved 99% accuracy, KNN with PCA reached 100%, and KNN with t-SNE attained 99%. Cross-validation was used to evaluate model generalization, yielding accuracies of 91%, 94%, and 91%, respectively. These findings suggest that KNN, particularly when integrated with t-SNE, is highly effective in visualizing and classifying non-linear data structures. Furthermore, this study demonstrates that incorporating dimensionalityreduction techniques enhances the interpretability of classified hypertension data, which is crucial for informed decision-making by mental healthcommittees

Andi Aulia Cahyana Resky1*, Jessica Crisfin Lapendy2, Andi Akram Nur Risal3*, Dewi Fatmarani Surianto4, Abdul Wahid

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6208/1025

Informatics and Computer Engineering, Engineering, Makassar State University, Makassar, Indonesia

28-02-2025

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