Algoritma ClusterMix K-Prototypes Untuk Menangkap Karakteristik Pasien Berdasarkan Variabel Penciri Mortalitas Pasien Dengan Gagal Jantung

Raditya Novidianto, Kartika Fithriasari

Abstract


Cardiovascular Disease  (CVD) atau penyakit kardiovaskular adalah salah satu penyebab utama kematian cukup besar di seluruh dunia yang berujung pada kejadian gagal jantung. Organiasasi kesehatan WHO menyebutkan jumlah orang yang  meninggal karena penyakit kardiovaskuler akibat gagal jantung setiap tahun memiliki rata-rata 17,9 juta kematian setiap tahunnya, yaitu sekitar 31 persen dari total kematian secara global. Pendeteksian faktor mortalitas pasien gagal jantung perlu dibentuk segmentasi yang berguna untuk memperkecil peluang terjadinya kematian akibat  gagal jantung. Salah satunya dengan menggunakan variabel penciri mortalitas akibat gagal jantung dengan cara menerapkan algoritma k-prototypes. Hasil penggerombolan terbentuk 2 kluster yang dianggap optimal berdasarkan nilai koefisien silhouette tertinggi yaitu sebesar 0.5777. Hasil penelitian dilakukan segementasi pasien dengan variabel penciri mortalitas pasien gagal jantung yang menunjukan bahwa kluster 1 merupakan gerombol pasien yang memiliki resiko rendah terhadap peluang mortalitas akibat gagal jantung dan kluster 2 merupakan gerombol pasien dengan karaktistik pasien dengan resiko yang tinggi terhadap peluang mortalitas akibat gagal jantung. Segementasi tersebut didasari dari nilai rata-rata setiap variabel penciri  dari faktor mortalitas gagal jantung pada setiap kluster yang dibandingkan dengan kondisi normal pada variabel serum creatine, ejection fraction, usia, serum sodium, tekanan darah, anemia, creatinine phosphokinase, plateles, merokok, jenis kelamin dan diabetes.

Keywords


Penyakit kardiovakuler, ClusterMix, Algoritma K-Prototype, Koefisien Silhouette

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DOI: http://dx.doi.org/10.12962/j27213862.v4i1.8479

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