Implementation of Microservice Architecture in a Machine Learning-Based Expert System for Sleep Disorder Diagnosis

Amin Nur Rais; Warjiyono

doi:10.59934/jaiea.v5i2.2065

Authors

Amin Nur Rais Universitas Bina Sarana Informatika
Warjiyono Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v5i2.2065

Keywords:

Gaussian Naive Bayes, Sleep Disorders, Microservice, Python Flask, CodeIgniter 3

Abstract

Sleep disorders constitute a significant health concern that frequently remains undiagnosed due to restricted access to adequate clinical assessment facilities. This study aims to develop an accurate and accessible early detection system for sleep disorders by implementing the Gaussian Naive Bayes algorithm within a hybrid microservice architecture. The development methodology involves decoupling the intelligent computing service, built on Python (Flask), from the user interface, developed using PHP (CodeIgniter 3), with communication facilitated via an Application Programming Interface (API). The model was trained utilizing a sleep disorder diagnostic dataset comprising 1,000 medical records and evaluated using the 10-Fold Cross-Validation method. Experimental results indicate that the developed model demonstrates superior classification performance, achieving an accuracy of 97.40% and a recall of 99.66%. The high recall value evidences the system's superior sensitivity in detecting positive cases, thereby effectively minimizing the risk of undetected patients (False Negatives). System integration via API proved stable in delivering real-time diagnostic visualization, confirming that this hybrid architecture offers a valid, modular, and responsive solution for the implementation of intelligent healthcare systems.

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