Abstract
Coronary heart disease is caused due to an accumulation of fat on the inside walls of blood vessels of the heart (coronary arteries). The factors that had led to the occurrence of coronary heart disease is dominated by unhealthy lifestyle of patients, and the survival times of different patients. This research objective is to predict the survival time of patients with coronary heart disease by taking into account the explanatory variables were analyzed by the method of Partial Least Square (PLS). PLS method is used to resolve the multiple regression analysis when the specific problems of multicollinearity and microarray data. The purpose of the PLS method is to predict the explanatory variables with multiple response variables so as to produce a more accurate predictive value. The results of this research showed that the prediction of survival for the three samples of patients with coronary heart disease had an average of 13 days, with a RMSEP value (error value) was 1.526 which means that the results of this study are not much different from the predicted results in the field of medicine. This is consistent with the fact that the medical field suggests that the average survival for patients with coronary heart disease by 13 days.
Highlights
Analisis survival merupakan alat yang digunakan untuk menganalisis data yang berhubungan dengan kondisi kegagalan atau pada waktu dari kondisi awal hingga berakhirnya kejadian atau titik akhir, Allison [1]
The factors that had led to the occurrence of coronary heart disease is dominated by unhealthy lifestyle
into account the explanatory variables were analyzed by the method of Partial Least Square
Summary
Analisis survival merupakan alat yang digunakan untuk menganalisis data yang berhubungan dengan kondisi kegagalan atau pada waktu dari kondisi awal (time origin) hingga berakhirnya kejadian atau titik akhir, Allison [1]. Metode Partial Least Square (PLS) digunakan ketika terjadi permasalahan spesifik pada data, seperti ukuran sampel yang akan diteliti kecil (microarray data), adanya data yang hilang (missing value), terjadinya korelasi antar peubah penjelas (multikolinearitas), terjadinya pendugaan yang bias (overfitting) dan data yang digunakan merupakan data yang waktu ketahanan hidupnya tidak diketahui secara pasti (data tersensor), Esposito et all [3] , Somnath dan Susmita [5]. Rumusan masalah dari penelitian ini adalah bagaimana memprediksi waktu ketahanan hidup pasien penyakit jantung koroner dengan memperhatikan peubah gen, jenis kelamin, usia, stress, kadar gula dalam darah, tekanan darah, jumlah batang rokok dihisap tiap hari, kolesterol, obesitas, olahraga dan rentang waktu saat pertama kali sakit hingga saat data diambil dalam penelitian (April 2011) dengan menggunakan metode Partial Least Square (PLS)?. Penelitian ini bertujuan memprediksi waktu ketahanan hidup pasien penyakit jantung koroner dengan memperhatikan peubah-peubah penjelas, menggunakan metode Partial Least Square (PLS)
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