ClusterMix K-Prototypes Algorithm to Capture Variable Characteristics of Patient Mortality With Heart Failure - Novidianto, Raditya; Wibowo, Hardianto; Chandranegara, Didih Rizki (Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 2021)
Cardiovascular Disease (CVD) is one of the leading causes of many death worldwide, leading to heart failure incidence. The World Health Organization (WHO) says the number of people dying from cardiovascular disease from heart failure each year has an average of 17,9 million deaths each year, about 31 percent of the total deaths globally. Identify the mortality factors of heart failure patients that need to be formed, which reduces death due to heart failure. One of them is by using variable mortality due to heart failure by applying the k-prototypes algorithm. The clustering result is formed 2 clusters that are considered optimal based on the highest silhouette coefficient value of 0,5777. The results of the study were carried out as segmentation of patients with variable mortality of heart failure patients, which showed that cluster 1 is a cluster of patients who have a low risk of the chance of mortality due to heart failure and cluster 2 is a cluster of patients with a high risk of mortality due to heart failure. The segmentation is based on the average value of each variable of heart failure mortality factor in each cluster compared to normal conditions in serum creatine variables, ejection fraction,  age,  serum sodium, blood pressure, anemia,  creatinine phosphokinase,  platelets, smoking, gender, and diabetes.