Abstract
The information defined in medical health data is researched based on machine learning-related algorithms. Also, this paper used random forest and other related algorithms to perform health data training and fitting. Research shows that the algorithm proposed in the paper can improve the progress of health data classification. The algorithm can provide technical support for the improvement of medical data classification.
Highlights
With the development of China’s medical industry, the medical market has become more and more complicated
Establishing a sound medical credit system is one of the important means to regulate the medical market. e lack of standard measures for participants in China’s medical market has led to frequent breaches of trust, such as registration breaches [1]. is seriously wastes limited medical resources. is article studies the dishonesty behaviour in medical treatment. e purpose of the research is to increase the medical industry’s management of market participants and improve the market access threshold and management level
When the scenario of the algorithm is in the medical field, the relevant historical behaviour data of medical market participants can be used to predict whether there is a risk of dishonesty. is assists medical market managers in making decisions
Summary
With the development of China’s medical industry, the medical market has become more and more complicated. Establishing a sound medical credit system is one of the important means to regulate the medical market. E lack of standard measures for participants in China’s medical market has led to frequent breaches of trust, such as registration breaches [1]. Is article studies the dishonesty behaviour in medical treatment. When the scenario of the algorithm is in the medical field, the relevant historical behaviour data of medical market participants can be used to predict whether there is a risk of dishonesty. Is paper studies the decision tree and random forest algorithm. Since the random forest algorithm is based on the ensemble learning idea, it effectively avoids noise in the training data set, so there will be no overfitting phenomenon. Since the random forest algorithm is based on the ensemble learning idea, it effectively avoids noise in the training data set, so there will be no overfitting phenomenon. e simulation results show that this method performs better than logistic regression and K-nearest algorithm in identifying dishonest behaviours [2]. is has important reference significance for the establishment of the medical credit system
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