In providing health services, health workers need information about the patient's disease risk to ensure that the services offered are by their needs. Meanwhile, the classification algorithm will train the system to predict disease risk information. This study will prove that using clustering Neural Network Self Organizing Maps (SOM) can increase the accuracy of predicting disease risk scores due to noisy features in the dataset. Clustering carried out at the preprocessing stage resulted in grouping disease features from 841 categories to 247 categories. The design of SOM clustering consists of a matrix of 15 x 20 neurons, 100 epochs, root means square performance, resulting in an accuracy rate of 93.7% in 47 seconds. In the training phase, 38,659 public data from Kaggle were applied, divided into seven age groups. In each age group, the system classifies disease risk scores into 11 risk score classes. The results of SOM clustering are used as predictors in the prediction system through experiments using five classification algorithms. Based on the results obtained, the Fine Tree Algorithm has the highest increase in accuracy for the entire dataset, from 99.1% to 99.8%.

Original languageEnglish
Title of host publicationInternational Electronics Symposium 2021
Subtitle of host publicationWireless Technologies and Intelligent Systems for Better Human Lives, IES 2021 - Proceedings
EditorsAndhik Ampuh Yunanto, Artiarini Kusuma N, Hendhi Hermawan, Putu Agus Mahadi Putra, Farida Gamar, Mohamad Ridwan, Yanuar Risah Prayogi, Maretha Ruswiansari
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665443463
Publication statusPublished - 29 Sept 2021
Event23rd International Electronics Symposium, IES 2021 - Surabaya, Indonesia
Duration: 29 Sept 202130 Sept 2021

Publication series

NameInternational Electronics Symposium 2021: Wireless Technologies and Intelligent Systems for Better Human Lives, IES 2021 - Proceedings


Conference23rd International Electronics Symposium, IES 2021


  • accuracy
  • and SOM clustering
  • disease risk score
  • noisy feature
  • prediction


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