ESDS: AI-Powered Early Stunting Detection and Monitoring System using Edited Radius-SMOTE Algorithm

TL;DR

This paper presents ESDS, an AI-powered system utilizing sensor data and a novel Radius-SMOTE algorithm for early detection and monitoring of stunting in children, addressing healthcare challenges in resource-limited Indonesian regions.

Contribution

The paper introduces a new AI system with sensor integration and a modified Radius-SMOTE algorithm for improved stunting detection accuracy.

Findings

Sensor sensitivities: load cell 0.9919, ultrasonic 0.9986

Machine learning accuracy for classifying stunting: 98%

Effective in resource-limited healthcare settings

Abstract

Stunting detection is a significant issue in Indonesian healthcare, causing lower cognitive function, lower productivity, a weakened immunity, delayed neuro-development, and degenerative diseases. In regions with a high prevalence of stunting and limited welfare resources, identifying children in need of treatment is critical. The diagnostic process often raises challenges, such as the lack of experience in medical workers, incompatible anthropometric equipment, and inefficient medical bureaucracy. To counteract the issues, the use of load cell sensor and ultrasonic sensor can provide suitable anthropometric equipment and streamline the medical bureaucracy for stunting detection. This paper also employs machine learning for stunting detection based on sensor readings. The experiment results show that the sensitivity of the load cell sensor and the ultrasonic sensor is 0.9919 and 0.9986, respectively. Also, the machine learning test results have three classification classes, which are normal, stunted, and stunting with an accuracy rate of 98\%.