Research on biomechanics integrated Bayesian network mental health diagnosis system
Abstract
With the rapid economic development of various countries around the world and the acceleration of global networking, countries are striving to promote their own urbanization and industrialization progress. The side effect is that social pressure leads to the concentrated outbreak of various social contradictions. The main psychological health testing and evaluation method in society is still conducted through dialogue with psychologists. Doctors obtain information through dialogue and communication with patients, and diagnose their psychological status based on this information. Affected by factors such as communication style and the patient’s own mental state. The information obtained may result in omissions and biases, leading to inaccurate diagnostic results. Bayesian network is a probabilistic graphical model that derives the results through information calculation. It can analyze and calculate the finite and incomplete conditions, carry out corresponding reasoning, and obtain more rigorous results. This article applied the naive Bayesian algorithm to the research of mental health diagnosis systems, and compared it with mental health diagnosis systems that do not use algorithms. According to the mental health index of contemporary people, the algorithm achievement test experiment of mental health diagnosis system was carried out. After research and comparison, it was found that for the collected data, the maximum accuracy of the Naive Bayesian algorithm within a hundred calculations reached 99%, with a mean of 96.5%. The traditional paper-based psychological diagnosis method had a maximum accuracy of 89%, a minimum of 70%, and an average accuracy of 80.5%. Therefore, the application of naive Bayesian network to the development and research of mental health diagnosis system can effectively improve the efficiency, accuracy and diagnostic effect of mental diagnosis.
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