Particle Swarm Optimization for Fuzzy Membership Functions Optimization. Mathematical and Computer Modelling, 52(11-12), pp. A PSO-Fuzzy Group Decision-Making Support System In Vehicle Performance Evaluation. Li Zhang, Liang Gao, Xinyu Shao, Long Wen, and Jun Zhi.IEEE International Conference, Yasmine Hammamet, Tunisia. Particle Swarm Optimization for Fuzzy Membership Functions Optimization, Systems, Man and Cybernetics.

ADVCOMP 2015: The Ninth International Conference on Advanced Engineering Computing and Applications in Sciences, 122-127, July 19 - 24, 2015, Nice, France. A Fuzzy-Genetic Algorithm Method for the Breast Cancer Diagnosis Problem. A New Fuzzy Modeling Framework for Integrated Risk Prognosis and Therapy of Bladder Cancer Patients.

Olusayo Obajemu, Mahdi Mahfouf, and James W.

Neural Computing and Applications 23, 471-483. Tuberculosis Disease Diagnosis by Using Adaptive Neuro Fuzzy Inference System and Rough Sets.

Tamer Uçar, Adem Karahoca, and Dilek Karahoca.

Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2016. Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System. Jamshid Norouzi, Ali Yadollahpour, Seyed A.Computer Methods and Programs in Biomedicine 161, 145-172. Diseases Diagnosis Using Fuzzy Logic Methods: A Systematic and Meta-Analysis Review. Hossein Ahmadi, Marsa Gholamzadeh, Leila Shahmoradi, Mehrbakhsh Nilashi, and Pooria Rashvand.Konsensus Pengendalian dan Pencegahan Diabetes MelitusTipe 2 di Indonesia 2015 (1st. Prosiding Seminar Nasional ASBIS 2017 Politeknik Negeri Banjarmasin. Uji Coba Metode Mamdani Untuk Deteksi Penyakit Diabetes di RSUD Dr. Slamet Riyadhi and Heru Kartika Candra.Diabetes Melitus Penyebab Kematian Nomor 6 di Dunia Retrieved Jfrom Google Scholar WHO Global report on diabetes: A summary. The resulting MSE value is very small, so this proves the high level of accuracy. The MSE value generated between the actual data and the proposed method is 0.012. Based on user data that has been processed using the proposed method, the prediction results are more accurate than the data processed using only Fuzzy Tsukamoto. Membership optimization is expected to increase the prediction results to be more accurate. The membership function for Fuzzy Tsukamoto will be optimized with PSO. This study uses a combination of Fuzzy Tsukamoto and PSO to predict the risk level of DM. The prediction of DM risk is based on the input variable consisting of age, body mass index and blood pressure (systolic) which are proven to be the basis for determining the risk ratio of DM and the output variable is the level of diabetes risk (low, high). By creating a prediction model the risk of DM can be identified in the early stage. Therefore, researchers conducted research to make predictions of the risk level of DM. The number of people with DM in Indonesia is a lot, which may increase the health costs and may cause trouble for health workers. Diabetes Mellitus (DM) is known as the silent killer because the sufferer often goes unnoticed and when it is known, complications usually already occur.

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