Sensor-based prediction of ALS progression: exploring PHI and feature engineering

Abstract

Amyotrophic lateral sclerosis (ALS) is a serious disease that affects nerve and muscle function, with no known cure. Early and accurate monitoring is essen tial to help physicians provide better care. Although machine learning has been applied to predict the progression of ALS, many models struggle with issues such as poor data quality and missing information, which affect accuracy. In this paper, our aim is to improve existing models by introducing better features to enhance prediction performance. A key contribution is the development of a new feature called the physical health index (PHI), which combines four im portant patient attributes: body mass index (BMI), weight, forced vital capacity (FVC), and basal calories. This feature provides a clearer view of the physical health of the patient, enabling the model to learn more effectively. We used the IDPP CLEF 2024 BTO dataset and performed three experiments: using 50 raw features, 29 engineered features, and 25 further engineered features including PHI. The results showed that the R-squared of the XGBoost model improved from 0.9573 to 0.9663 and finally 0.9828, while RMSE decreased from 0.2317 to 0.1801 and then 0.1182 with PHI. This study highlights how targeted feature engineering can improve the prediction of ALS using machine learning.