Semi-Supervised Regression with Co-learning using Optimal Transport for Predicting Future Health Checkup Results
Yuki Kosaka, Keisuke Suzuki, Kosuke Nishihara, Mana Hashimoto, Fumiyuki Nihey, Kentaro Nakahara · 2025
This study introduces a co-learning style semi-supervised regression (SSR) to predict future health checkup results. Co-learning is a significant approach within SSR, which builds a model based on labeled samples and estimates target response assigned to unlabeled samples as pseudo-labels, which are then used as labeled samples during training. Recently, this approach has emphasized the importance of enhancing pseudo-labels’ quality. However, conventional SSR generates pseudo-labels using only given labeled and unlabeled samples, limiting their quality. Therefore, incorporating auxiliary data and diverse perspectives is crucial for generating reliable pseudo-labels.We present a new co-learning style SSR using auxiliary data from health checkup results accumulated across different age groups. Using optimal transport (OT) theory, we learn a map of the unpaired distributions of health checkup results from different age groups. This OT map assigns target responses to unlabeled samples as pseudo-labels. Our approach assumes that change in health checkup results over time can be inferred by comparing them collected in different age groups despite being different individuals. By integrating pseudo-labels assigned from labeled and unlabeled samples and ones assigned using our OT map from an auxiliary view in an optimally weighted manner, we improve the quality of pseudo-labels to enhance prediction accuracy. Experimental results using health checkup results from a large cohort study over fifteen years show that our method using auxiliary data with an OT map is effective in achieving higher accuracy than the existing co-learning approach on SSR.