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All Journal International Journal of Artificial Intelligence Research
Annisa Eka Haryati
Universitas Ahmad Dahlan Yogyakarta
Computer Science & IT Electrical & Electronics Engineering

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Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering Annisa Eka Haryati; sugiyarto - surono; Tommy Tanu Wijaya; Goh Khang Wen; Aris Thobirin
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (674.315 KB) | DOI: 10.29099/ijair.v7i1.306

Abstract

Objective: Fuzzy clustering algorithm is a partition method used to assign objects from a data set to a cluster by marking the average location. Furthermore, Fuzzy Subtractive Clustering (FSC) with hamming distance and exponential membership function is used to analyze the cluster center of a data point. Therefore, the purpose of this research is to determine the number of clusters with the best quality by comparing the Partition Coefficient (PC) values for each number produced. Methods: The data set which is heart failure patient data is 150 data obtained from UCI Machine Learning. The data consists of 11 variables, including age , anemia , creatinine phosphokinase , diabetes ejection fraction , high blood pressure , platelets , serum creatinine , serum sodium , gender , and smoke . It simulated and processed using Fuzzy Subtractive Clustering Algorithm, Jupyter Notebook Software with Python programming language. Result: The results showed that the most optimal number of clusters is 3, which are selected based on the largest PC value. Conclusion: Based on the results obtained, the highest P value is in cluster 3, therefore heart failure can be grouped into 3, namely low, moderate, severe.
Co-Authors Aris Thobirin Goh Khang Wen sugiyarto - surono Tommy Tanu Wijaya