Hybrid imputation of biomedical data by using transformers and autoencoders for assessing human biological age - Slipchenko, Volodymyr; Poliahushko, Liubov; Volkov, Oleksandr; Shatylo, Vladyslav (Eastern-European Journal of Enterprise Technologies, 2025)
This study investigates the process of restoring missing biomedical and social data for human biological age assessment. The principal challenge is the high rate of missing values in datasets, notably NHANES – up to 40%. This complicates accurate health prediction and reduces the effectiveness of preventive interventions. To address this issue, deep learning methods, specifically autoencoders and transformers, were employed. The autoencoder provided fast imputation (37.4 s, MAE = 7.54) but less accuracy. The transformer achieved the highest accuracy (246.3 s, MAE = 1.10) yet required substantial resources and showed overfitting risks. A hybrid architecture has been proposed to combine the advantages of both approaches. On the NHANES dataset (55,081 records and 84 biomarkers), the model demonstrated an optimal balance (54.2 s, MAE = 5.26) and stability with up to 50% missing data. Compared to mean-value imputation, the accuracy of biological age estimation improved by 25%. The coefficient of determination reached 0.9875, and root mean squared error was 35.9, confirming strong consistency of the restored values. Sensitivity analysis revealed stable accuracy up to 55% missing data, after which degradation occurred. A unique feature of the hybrid approach is the combination of high accuracy with moderate computational cost. This makes the model suitable for medical information systems with incomplete datasets. Practical applications include preventive medicine, biological aging monitoring, and risk group identification. In the Ukrainian context, the model could enhance biomedical research and digital healthcare while also serving as a foundation for bioinformatics and life expectancy studies