Skeletal muscle early injury time prediction method based on Stacking ensemble learning

Abstract

The invention relates to the field of forensic medicine, in particular to a skeletal muscle early injury time prediction method based on Stacking ensemble learning, which comprises the following steps: collecting skeletal muscle samples of rats at different injury time, and obtaining skeletal muscle injury repair related gene expression quantity; according to the prediction models of the three base classifiers, stacking the prediction probability values of the three base classifiers to form a new feature set, and conducting training to obtain a final Stacking ensemble learning model; and inputting the data of an unknown sample into the Stacking ensemble learning model so as to predict the damage time of the unknown sample. According to the prediction method, prediction results of the three base classifiers are integrated by adopting Stacking ensemble learning, and the three base classifiers are subjected to parameter optimization through grid search and cross validation, so that the accuracy and stability of skeletal muscle early injury time deduction are effectively improved.

Description

technical field [0001] The invention relates to the field of forensic medicine, in particular to a method for predicting early injury time of skeletal muscle based on Stacking integrated learning. Background technique [0002] In forensic practice and research, accurate estimation of injury time has always been a key issue that needs to be solved urgently, especially in the early stage of injury. Because the body's life response does not change significantly, it is more difficult to estimate the early injury time. Generally speaking, when human tissues are mechanically injured, a series of characteristic changes such as bleeding, wounds, inflammatory reactions, and enzyme activity changes are often formed on the body surface and tissues. However, for individuals who die immediately after injury or have a short survival time, it is difficult to make a more accurate inference of the injury time only based on life responses. With the development of biological technology, the r...

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