Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA
(1) Background: Accurate diagnosis of wound age is crucial for investigating violent cases in forensic practice. However, effective biomarkers and forecast methods are lacking. (2) Methods: Samples were collected from rats divided randomly into control and contusion groups at 0, 4, 8, 12, 16, 20, an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/3/395 |
| _version_ | 1797624912694738944 |
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| author | Lihong Dang Jian Li Xue Bai Mingfeng Liu Na Li Kang Ren Jie Cao Qiuxiang Du Junhong Sun |
| author_facet | Lihong Dang Jian Li Xue Bai Mingfeng Liu Na Li Kang Ren Jie Cao Qiuxiang Du Junhong Sun |
| author_sort | Lihong Dang |
| collection | DOAJ |
| description | (1) Background: Accurate diagnosis of wound age is crucial for investigating violent cases in forensic practice. However, effective biomarkers and forecast methods are lacking. (2) Methods: Samples were collected from rats divided randomly into control and contusion groups at 0, 4, 8, 12, 16, 20, and 24 h post-injury. The characteristics of concern were nine mRNA expression levels. Internal validation data were used to train different machine learning algorithms, namely random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), gradient boosting (GB), and stochastic gradient descent (SGD), to predict wound age. These models were considered the base learners, which were then applied to developing 26 stacking ensemble models combining two, three, four, or five base learners. The best-performing stacking model and base learner were evaluated through external validation data. (3) Results: The best results were obtained using a stacking model of RF + SVM + MLP (accuracy = 92.85%, area under the receiver operating characteristic curve (AUROC) = 0.93, root-mean-square-error (RMSE) = 1.06 h). The wound age prediction performance of the stacking models was also confirmed for another independent dataset. (4) Conclusions: We illustrate that machine learning techniques, especially ensemble algorithms, have a high potential to be used to predict wound age. According to the results, the strategy can be applied to other types of forensic forecasts. |
| first_indexed | 2024-03-11T09:49:22Z |
| format | Article |
| id | doaj.art-a00f1c192ecd4f1e881e931cacb32d90 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T09:49:22Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-a00f1c192ecd4f1e881e931cacb32d902023-11-16T16:24:06ZengMDPI AGDiagnostics2075-44182023-01-0113339510.3390/diagnostics13030395Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNALihong Dang0Jian Li1Xue Bai2Mingfeng Liu3Na Li4Kang Ren5Jie Cao6Qiuxiang Du7Junhong Sun8School of Forensic Medicine, Shanxi Medical University, 98 University Street, Yuci District, Jinzhong 030604, ChinaSchool of Forensic Medicine, Shanxi Medical University, 98 University Street, Yuci District, Jinzhong 030604, ChinaSchool of Forensic Medicine, Shanxi Medical University, 98 University Street, Yuci District, Jinzhong 030604, ChinaSchool of Forensic Medicine, Shanxi Medical University, 98 University Street, Yuci District, Jinzhong 030604, ChinaSchool of Forensic Medicine, Shanxi Medical University, 98 University Street, Yuci District, Jinzhong 030604, ChinaSchool of Forensic Medicine, Shanxi Medical University, 98 University Street, Yuci District, Jinzhong 030604, ChinaSchool of Forensic Medicine, Shanxi Medical University, 98 University Street, Yuci District, Jinzhong 030604, ChinaSchool of Forensic Medicine, Shanxi Medical University, 98 University Street, Yuci District, Jinzhong 030604, ChinaSchool of Forensic Medicine, Shanxi Medical University, 98 University Street, Yuci District, Jinzhong 030604, China(1) Background: Accurate diagnosis of wound age is crucial for investigating violent cases in forensic practice. However, effective biomarkers and forecast methods are lacking. (2) Methods: Samples were collected from rats divided randomly into control and contusion groups at 0, 4, 8, 12, 16, 20, and 24 h post-injury. The characteristics of concern were nine mRNA expression levels. Internal validation data were used to train different machine learning algorithms, namely random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), gradient boosting (GB), and stochastic gradient descent (SGD), to predict wound age. These models were considered the base learners, which were then applied to developing 26 stacking ensemble models combining two, three, four, or five base learners. The best-performing stacking model and base learner were evaluated through external validation data. (3) Results: The best results were obtained using a stacking model of RF + SVM + MLP (accuracy = 92.85%, area under the receiver operating characteristic curve (AUROC) = 0.93, root-mean-square-error (RMSE) = 1.06 h). The wound age prediction performance of the stacking models was also confirmed for another independent dataset. (4) Conclusions: We illustrate that machine learning techniques, especially ensemble algorithms, have a high potential to be used to predict wound age. According to the results, the strategy can be applied to other types of forensic forecasts.https://www.mdpi.com/2075-4418/13/3/395wound age estimationskeletal muscle contusionstacking ensemble learningmultiple mRNAsforensic science |
| spellingShingle | Lihong Dang Jian Li Xue Bai Mingfeng Liu Na Li Kang Ren Jie Cao Qiuxiang Du Junhong Sun Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA Diagnostics wound age estimation skeletal muscle contusion stacking ensemble learning multiple mRNAs forensic science |
| title | Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA |
| title_full | Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA |
| title_fullStr | Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA |
| title_full_unstemmed | Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA |
| title_short | Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA |
| title_sort | novel prediction method applied to wound age estimation developing a stacking ensemble model to improve predictive performance based on multi mrna |
| topic | wound age estimation skeletal muscle contusion stacking ensemble learning multiple mRNAs forensic science |
| url | https://www.mdpi.com/2075-4418/13/3/395 |
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