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Semi-supervised learning with dynamic classifier selection for PMI estimation: An animal study
Jian Li1, Yan-Juan Wu1, Xing-Yu Lu1
1School of Forensic Medicine, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, Shanxi Province, 030604, China; Shanxi Key Laboratory of Forensic Medicine, Jinzhong, 030600, Shanxi, China.
Abstract:
The application of machine learning in analyzing postmortem molecular alterations represents a promising strategy for estimating the Postmortem Interval (PMI), yet samples with undetermined PMI significantly compromise model efficacy and compound the existing scarcity of forensically viable samples. The generalization ability of the model is significantly impaired by the insufficient effective sample size, which greatly hinders the widespread application of PMI estimation based on machine learning. Semi-supervised learning (SSL) methods can effectively leverage both labeled samples with known PMI and unlabeled samples with unknown PMI to jointly train models. This approach is crucial for enhancing sample utilization and improving the accuracy of PMI estimation. In this study, we examined skeletal muscle from rats at different PMI. Using both labeled and unmarked PMI samples, we evaluated the efficacy of Supervised Learning (SL) and SSL models in addressing the prevalent challenge of PMI estimation within forensic science. On this basis, we employed the dynamic classifier selection (DCS) strategy to enhance the construction process of both SL and SSL models. The findings demonstrate that SSL-DCS significantly enhances the predictive efficacy of PMI compared to SL-DCS, exhibiting an area under receiver operating characteristic curve (AUC) of 0.89 and an R2 of 0.93 for time to death from 0 to 9 days. The outcomes of this study suggest that SSL-DCS not only enhances prediction efficacy but also improves the utilization rate of scarce samples, which would serve as a paradigm that can be extended and applied to other biomedical domains.
Insights
Semi-supervised learning with dynamic classifier selection (SSL-DCS) improves machine learning models for estimating the postmortem interval (PMI). This approach effectively utilizes limited samples, enhancing accuracy in forensic science applications.
Area of Science:
- Forensic Science
- Machine Learning
- Molecular Biology
Background:
- Machine learning for postmortem interval (PMI) estimation faces challenges due to limited labeled samples.
- Undetermined PMI samples reduce model efficacy and exacerbate sample scarcity in forensic investigations.
Purpose of the Study:
- To evaluate supervised learning (SL) and semi-supervised learning (SSL) models for PMI estimation using skeletal muscle samples.
- To enhance SL and SSL models with dynamic classifier selection (DCS) for improved performance.
Main Methods:
- Utilized rat skeletal muscle samples with known and unknown PMI.
- Compared the efficacy of SL and SSL models, both with and without DCS.
- Assessed model performance using metrics like AUC and R-squared.
Main Results:
- SSL-DCS significantly outperformed SL-DCS in PMI prediction.
- SSL-DCS achieved an AUC of 0.89 and an R-squared of 0.93 for estimating time to death within 0-9 days.
- The study demonstrated enhanced predictive efficacy and improved utilization of scarce samples.
Conclusions:
- SSL-DCS offers a superior approach for machine learning-based PMI estimation.
- This method effectively addresses the challenge of limited data in forensic science.
- The SSL-DCS paradigm shows potential for application in other biomedical fields.
