用传感器+机器学习实现印尼儿童发育迟缓早期精准筛查
ESDS: AI-Powered Early Stunting Detection and Monitoring System using Edited Radius-SMOTE Algorithm
- 结合称重与超声波传感器采集生长数据，自动完成体征测量
- 机器学习模型对三类状态识别准确率达98%，灵敏度超99%
- 适合资源有限地区推广，降低对专业人员依赖
发育迟缓是印度尼西亚医疗体系中的重大挑战，导致认知能力下降、生产力减弱、免疫力降低、神经发育延迟及退行性疾病。在高发地区且资源匮乏的背景下，及时识别需治疗儿童至关重要。传统诊断面临医务人员经验不足、体格测量设备不兼容及医疗流程低效等问题。本文采用称重传感器和超声波传感器作为合适的体格测量工具，并通过机器学习模型基于传感器读数进行发育迟缓检测。实验结果显示，称重传感器和超声波传感器的灵敏度分别为0.9919和0.9986。机器学习分类模型在正常、发育迟缓、潜在发育迟缓三类中的准确率为98%。
原文摘要 · Abstract (English)
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\%.
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