Klasifikasi Status Tekanan Darah memanfaatkan Sinyal Photoplethysmograph berbasis Metode Random Forest

UNANG SUNARYA, LYRA VEGA UGI

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ABSTRAK

Berbagai teknik pengukuran tekanan darah telah banyak dilakukan salah satunya melalui metode tidak langsung (noninvasive) dengan pemasangan sensor-sensor pada bagian tubuh tertentu, kemudian hasilnya dianalisis dengan algoritma kecerdasan buatan. Namun, masih terdapat banyak kendala pada pemilihan algoritma yang tepat untuk mencapai hasil akurasi klasifikasi yang tinggi. Pada penelitian ini dilakukan klasifikasi status tekanan darah dengan menggunakan sinyal photoplethysmograph (PPG) yang pengukurannya dilakukan secara noninvasive dari 219 pasien. Algoritma random forest digunakan untuk mengklasifikasikan status pasien ke dalam empat kelas yaitu normal, prehypertension, stage 1 prehypertension dan stage 2 prehypertension. Untuk perbandingan, dataset juga diklasifikasikan dengan algoritma KNN dan SVM. Hasil menunjukkan bahwa algoritma random forest memberikan kinerja terbaik dengan akurasi sebesar 98,63%, presisi 98,72% dan recall 98.60%.

Kata kunci: tekanan darah, CVD, random forest, KNN, SVM

 

ABSTRACT

Various ways for measuring blood pressure have been employed, including noninvasive techniques that include placing senosrs on specific body areas and analyzing the finding using artificial intelligence algorithms. Nevertheless, there are numerous challenges in choosing the appropriate algorithms that yiled high accuracy in classification. In this study, blood pressure status was classified using photoplethysmograph (PPG) signals, which were measured non-invasively from 219 patients. The random forest algorithm was used to classify patient status into four classes, namely normal, prehypertension, prehypertension stage 1 and prehypertension stage 2. For comparison, the dataset was also classified using the KNN and SVM algorithms. The results show that the random forest algorithm provides the best performance with an accuracy of 98.63%, precision of 98.72% and recall of 98.60%, respectively.

Keywords: blood pressure, CVD, random forest, KNN, SVM


Kata Kunci


tekanan darah; CVD; random forest; KNN; SVM

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Referensi


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DOI: https://doi.org/10.26760/elkomika.v12i3.687

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