Optimization of Sleep Disorder Classification Using ANN with Multi-Method Feature Selection

Authors

  • Devi Nova Kharisma Nahdlatul Ulama Sunan Giri University, Indonesia
  • Ifnu Wisma Dwi Prastya Nahdlatul Ulama Sunan Giri University, Indonesia
  • Ita Aristia Saida Nahdlatul Ulama Sunan Giri University, Indonesia
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DOI:

https://doi.org/10.63158/journalisi.v8i2.1473

Keywords:

Artificial Neural Network, Sleep Disorder Classification, Multiclass Classification, Sleep Health Dataset, Feature Selection

Abstract

Sleep disorders are health problems that can affect quality of life and have the potential to increase the risk of various chronic diseases. Therefore, a computational approach is needed to accurately and efficiently classify sleep disorders. The ANN model used has a two-layer hidden architecture with 128 and 64 neurons, respectively, and uses the ReLU activation function, equipped with a dropout layer to reduce overfitting. Three neurons with a softmax activation function make up the output layer, which produces probabilities for every class. To improve model performance, three feature selection methods were compared, namely Chi-Square, Information Gain, and Pearson Correlation. The test results showed that the ANN model without feature selection produced an accuracy of 89.3%. After feature selection, the model's performance improved significantly. The Chi-Square method produced 8 selected features with the highest accuracy of 97.3%, followed by Information Gain with 5 features and an accuracy of 97.3%, and Pearson Correlation with 3 features and an accuracy of 88.0%. The results of this study demonstrate that selecting appropriate features can significantly enhance an ANN's ability to categorize sleep problems. The proposed approach is expected to be a reference in the development of a more accurate sleep disorder diagnostic aid system.

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Published

2026-04-26

Issue

Vol. 8 No. 2 (2026): April

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Articles

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How to Cite

[1]
D. N. Kharisma, I. W. D. Prastya, and I. A. Saida, “Optimization of Sleep Disorder Classification Using ANN with Multi-Method Feature Selection”, journalisi, vol. 8, no. 2, pp. 2363–2381, Apr. 2026, doi: 10.63158/journalisi.v8i2.1473.