Data mining-based study on sub-mentally healthy state among residents in eight provinces and cities in China.

OBJECTIVE: To apply data mining methods to research on the state of sub-mental health among residents in eight provinces and cities in China and to mine latent knowledge about many conditions through data mining and analysis of data on 3970 sub-mentally healthy individuals selected from 13385 relevant questionnaires. METHODS: The strategic tree algorithm was used to identify the main manifestations of the state of sub-mental health. The back propogation artificial neural network was used to analyze the main manifestations of sub-healthy mental states of three different degrees. A sub-mental health evaluation model was then established to achieve predictive evaluation results. RESULTS: Using classifications from the Scale of Chinese Sub-healthy State, the main manifestations of sub-mental health selected using the strategic tree were F1101 (Do you lack peace of mind?), F1102 (Are you easily nervous when something comes up?), and F1002 (Do you often sigh?). The relative intensity of manifestations of sub-mental health was highest for F1101, followed by F1102, and then F1002. Through study of the neural network, better differentiation could be made between moderate and severe and between mild and severe states of sub-mental health. The differentiation between mild and moderate sub-mental health states was less apparent. Additionally, the sub-mental health state evaluation model, which could be used to predict states of sub-mental health of different individuals, was established using F1101, F1102, F1002, and the mental self-assessment total score. CONCLUSION: The main manifestations of the state of sub-mental health can be discovered using data mining methods to research and analyze the latent laws and knowledge hidden in research evidence on the state of sub-mental health. The state of sub-mental health of different individuals can be rapidly predicted using the model established here. This can provide a basis for assessment and intervention for sub-mental health. It can also replace the relatively outdated approaches to research on sub-health in the technical era of information and digitization by combining the study of states of sub-mental health with information techniques and by further quantifying the relevant information.