微型电脑应用

为了提高心理障碍预测准确性,提出了基于历史数据驱动的心理障碍预测模型。首先采集心理障碍的相关历史数据,对历史数据进行预处理,并提取一些关键属性;然后根据这些关键属性,采用隐马尔科夫方法对属性进行数据挖掘,建立心理障碍预测模型;进行心理障碍预测实验。结果表明,这一模型的心理障碍预测精度高,降低了心理障碍预测误差。

In order to improve the accuracy of mental disorder prediction, this paper proposes a mental disorder prediction model based on historical data. Firstly, the relevant historical data of mental disorder are collected, the historical data are preprocessed, and some key attributes are extracted. Then, according to these key attributes, the hidden Markovian method is used to mine the attributes, and a mental disorder prediction model is established. The last one is the experiment of mental disorder prediction. The results show that the prediction accuracy of this model is high and the prediction error is reduced.