Key result
An artificial neural network model predicted panic disorder with an AUC of 0.8255, demonstrating superior accuracy compared to two other machine learning models.
Why the study?
Panic disorder is a serious refractory condition with high incidence, and untimely treatment causes significant harm, motivating the development of machine learning prediction models.
Does an artificial neural network model accurately predict panic disorder compared to other machine learning models?
Does an artificial neural network model accurately predict panic disorder compared to other machine learning models?
An artificial neural network model demonstrates good predictive accuracy (AUC 0.8255) and efficiency for predicting panic disorder.
ANN shows promise for panic disorder prediction in model development; leaves open clinical utility pending prospective validation.
With the changes in people's living habits and environment, some mental illnesses have also appeared. Panic disorder is a serious refractory mental illness. Epidemiological research results show that: 5% of the world's people meet the clinical diagnosis of panic disorder, and about 10% of people have subclinical symptoms. It can be seen that the incidence of panic disorder is still relatively high. A family study found that 9% of teenagers had clinical symptoms of panic disorder. Patients with panic disorder will have symptoms such as panic, tremor, chest pain, and palpitations. If panic disorder patients do not get proper treatment in time, it will cause great harm to individuals, families and society. Therefore, it is very meaningful to predict panic disorder through machine learning algorithm analysis model. The main research content of this paper is the analysis of the prediction model of panic disorder by machine learning algorithms. The research purpose of machine learning algorithms is to use computers to simulate human learning activities. It is a method to locate existing knowledge, acquire new knowledge, continuously improve performance, and achieve self-improvement by learning computers. This article analyzes the relevant theories and characteristics of machine learning algorithms and integrates them into panic disorder prediction analysis. The final results of the study show that the AUC of the artificial neural network model is the largest, reaching 0.8255, indicating that it is better than the other two models in prediction accuracy. In terms of running time, the time of the three models is less than 1 second, which is within the acceptable range.
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Meng et al. (2021) studied Panic disorder. Artificial neural network model vs. Other machine learning models was evaluated on Prediction accuracy (AUC) (AUC 0.8255). An artificial neural network model predicted panic disorder with an AUC of 0.8255, demonstrating superior accuracy compared to two other machine learning models.
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