An Affordable phototherapy intensity meter using machine learning to improve the quality of care system for Hyperbilirubinemia in Indonesia

Abstract

Hyperbilirubinemia is more frequently seen in low and middle-income countries like Indonesia. One of the contributing factors is a substandard dose of Phototherapy irradiance. This research aims to design a phototherapy intensity meter called PhotoInMeter using readily available low-cost components. PhotoInMeter is designed by using a microcontroller, light sensor, color sensor, and an ND (neutral-density) filter. We use machine learning to create a mathematical model that converts the emission from the color sensor and light sensor into light intensity measurements that are close to Ohmeda Biliblanket’s measurements. Our prototype collects sensor reading data and pairs them with Ohmeda Biliblanket Light Meter to create a training set for our machine learning algorithm. We create a multivariate linear regression, random forest, and XGBoost model based on our training set to convert sensor readings to Ohmeda Biliblanket Light Meter measurement. We successfully devised a prototype that costs 20 times less to produce compared to our reference intensity meter while still having high accuracy. Compared to Ohmeda Biliblanket Light Meter, our PhotoInMeter has a Mean Absolute Error (MAE) of 0.83 and achieves more than a 0.99 correlation score in all six different devices for intensity in the range of 0–90 μW/cm2/nm. Our prototypes show consistent reading between PhotoInMeter devices, having an average difference of 0.435 among all six devices.