Application of the decision tree method in forensic-medical practice in the analysis of 'doctors proceedings'

Franchuk, V. V.; Mikhaylichenko, B. V.; Franchuk, M. V.

doi:10.17116/sudmed2020630119

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Decision Trees [37][38][39] are one of the most powerful algorithmic approaches currently used in the vast majority of Machine Learning research fields, especially in the fields of healthcare and data mining or data science, which search for different behaviors that can define efficient and more accurate sets of patterns, that are applicable to the general population.…”

Section: Decision Tree Classification Analysismentioning

confidence: 99%

Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques

Boukichou-Abdelkader,

Montero-Alonso,

Muñoz-García

2024

Computation

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Recently, many methods and algorithms have been developed that can be quickly adapted to different situations within a population of interest, especially in the health sector. Success has been achieved by generating better models and higher-quality results to facilitate decision making, as well as to propose new diagnostic procedures and treatments adapted to each patient. These models can also improve people’s quality of life, dissuade bad health habits, reinforce good habits, and modify the pre-existing ones. In this sense, the objective of this study was to apply supervised and unsupervised classification techniques, where the clustering algorithm was the key factor for grouping. This led to the development of three optimal groups of clinical pattern based on their characteristics. The supervised classification methods used in this study were Correspondence (CA) and Decision Trees (DT), which served as visual aids to identify the possible groups. At the same time, they were used as exploratory mechanisms to confirm the results for the existing information, which enhanced the value of the final results. In conclusion, this multi-technique approach was found to be a feasible method that can be used in different situations when there are sufficient data. It was thus necessary to reduce the dimensional space, provide missing values for high-quality information, and apply classification models to search for patterns in the clinical profiles, with a view to grouping the patients efficiently and accurately so that the clinical results can be applied in other research studies.

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Section: Decision Tree Classification Analysismentioning

confidence: 99%

Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques

Boukichou-Abdelkader,

Montero-Alonso,

Muñoz-García

2024

Computation

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“…Machine learning is widely used in numerous fields of medical diagnostics and prognostics [ 39 , 40 , 41 , 42 ]. One possible method of estimating parameters is finding them by using the method of linear regression.…”

Section: Introductionmentioning

confidence: 99%

Beyond Henssge’s Formula: Using Regression Trees and a Support Vector Machine for Time of Death Estimation in Forensic Medicine

et al. 2023

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Henssge’s nomogram is a commonly used method to estimate the time of death. However, uncertainties arising from the graphical solution of the original mathematical formula affect the accuracy of the resulting time interval. Using existing machine learning techniques/tools such as support vector machines (SVMs) and decision trees, we present a more accurate and adaptive method for estimating the time of death compared to Henssge’s nomogram. Using the Python programming language, we built a synthetic data-driven model in which the majority of the selected tools can estimate the time of death with low error rates even despite having only 3000 training cases. An SVM with a radial basis function (RBF) kernel and AdaBoost+SVR provided the best results in estimating the time of death with the lowest error with an estimated time of death accuracy of approximately ±20 min or ±9.6 min, respectively, depending on the SVM parameters. The error in the predicted time (tp[h]) was tp±0.7 h with a 94.45% confidence interval. Because training requires only a small quantity of data, our model can be easily customized to specific populations with varied anthropometric parameters or living in different climatic zones. The errors produced by the proposed method are a magnitude smaller than any previous result.

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The Application of Decision Tree ID3 Algorithm in the Analysis of Enterprise Marketing Strategy

Zhong

2023

Lecture Notes on Data Engineering and Communications Technologies

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Application of the decision tree method in forensic-medical practice in the analysis of 'doctors proceedings'

Cited by 5 publications

References 7 publications

Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques

Group Classification for the Search and Identification of Related Patterns Using a Variety of Multivariate Techniques

Beyond Henssge’s Formula: Using Regression Trees and a Support Vector Machine for Time of Death Estimation in Forensic Medicine

The Application of Decision Tree ID3 Algorithm in the Analysis of Enterprise Marketing Strategy

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