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| Auteurs principaux: | , , , |
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| Format: | Preprint |
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2024
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| Accès en ligne: | https://arxiv.org/abs/2409.09056 |
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| _version_ | 1866916393652322304 |
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| author | Nau, Anna-Maria Ditto, Phillip Steadman, Dawnie Wolfe Mockus, Audris |
| author_facet | Nau, Anna-Maria Ditto, Phillip Steadman, Dawnie Wolfe Mockus, Audris |
| contents | Accurately assessing the postmortem interval (PMI) is an important task in forensic science. Some of the existing techniques use regression models that use a decomposition score to predict the PMI or accumulated degree days (ADD), however, the provided formulas are based on very small samples and the accuracy is low. With the advent of Big Data, much larger samples can be used to improve PMI estimation methods. We, therefore, aim to investigate ways to improve PMI prediction accuracy by (a) using a much larger sample size, (b) employing more advanced linear models, and (c) enhancing models with factors known to affect the human decay process. Specifically, this study involved the curation of a sample of 249 human subjects from a large-scale decomposition dataset, followed by evaluating pre-existing PMI/ADD formulas and fitting increasingly sophisticated models to estimate the PMI/ADD. Results showed that including the total decomposition score (TDS), demographic factors (age, biological sex, and BMI), and weather-related factors (season of discovery, temperature history, and humidity history) increased the accuracy of the PMI/ADD models. Furthermore, the best performing PMI estimation model using the TDS, demographic, and weather-related features as predictors resulted in an adjusted R-squared of 0.34 and an RMSE of 0.95. It had a 7% lower RMSE than a model using only the TDS to predict the PMI and a 48% lower RMSE than the pre-existing PMI formula. The best ADD estimation model, also using the TDS, demographic, and weather-related features as predictors, resulted in an adjusted R-squared of 0.52 and an RMSE of 0.89. It had an 11% lower RMSE than the model using only the TDS to predict the ADD and a 52% lower RMSE than the pre-existing ADD formula. This work demonstrates the need (and way) to incorporate demographic and environmental factors into PMI/ADD estimation models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09056 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identifying Factors to Help Improve Existing Decomposition-Based PMI Estimation Methods Nau, Anna-Maria Ditto, Phillip Steadman, Dawnie Wolfe Mockus, Audris Machine Learning Applications Accurately assessing the postmortem interval (PMI) is an important task in forensic science. Some of the existing techniques use regression models that use a decomposition score to predict the PMI or accumulated degree days (ADD), however, the provided formulas are based on very small samples and the accuracy is low. With the advent of Big Data, much larger samples can be used to improve PMI estimation methods. We, therefore, aim to investigate ways to improve PMI prediction accuracy by (a) using a much larger sample size, (b) employing more advanced linear models, and (c) enhancing models with factors known to affect the human decay process. Specifically, this study involved the curation of a sample of 249 human subjects from a large-scale decomposition dataset, followed by evaluating pre-existing PMI/ADD formulas and fitting increasingly sophisticated models to estimate the PMI/ADD. Results showed that including the total decomposition score (TDS), demographic factors (age, biological sex, and BMI), and weather-related factors (season of discovery, temperature history, and humidity history) increased the accuracy of the PMI/ADD models. Furthermore, the best performing PMI estimation model using the TDS, demographic, and weather-related features as predictors resulted in an adjusted R-squared of 0.34 and an RMSE of 0.95. It had a 7% lower RMSE than a model using only the TDS to predict the PMI and a 48% lower RMSE than the pre-existing PMI formula. The best ADD estimation model, also using the TDS, demographic, and weather-related features as predictors, resulted in an adjusted R-squared of 0.52 and an RMSE of 0.89. It had an 11% lower RMSE than the model using only the TDS to predict the ADD and a 52% lower RMSE than the pre-existing ADD formula. This work demonstrates the need (and way) to incorporate demographic and environmental factors into PMI/ADD estimation models. |
| title | Identifying Factors to Help Improve Existing Decomposition-Based PMI Estimation Methods |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2409.09056 |