Department of Pulmonary and Critical Care Medicine, Maoming People's Hospital, Maoming, Guangdong 525000, China.
Comput Intell Neurosci. 2022 May 31;2022:1198581. doi: 10.1155/2022/1198581. eCollection 2022.
Idiopathic interstitial pneumonia (IIP) is a group of progressive lower respiratory tract diseases of unknown origin characterized by diffuse alveolitis and alveolar structural disorders leading to pulmonary fibrillation and hypertension, pulmonary heart disease, and right heart failure due to pulmonary fibrosis, and more than half of them die from respiratory failure. To address these problems of overly complex prediction methods and large data sets involved in the prediction process of interstitial pneumonia, this paper proposes a prediction model for interstitial pneumonia which is based on the Gaussian Parsimonious Bayes algorithm. Three usual tests of pneumonia, specifically from various patients, were collected as the sample set. These samples are divided into training and testing sets. Additionally, a cross-validation strategy was used to avoid the overfitting problem. The results showed that the prediction model based on the Gaussian Parsimonious Bayes algorithm predicted 92% accuracy on the test set, and the Parsimonious Bayes method could directly predict the final detection of interstitial pneumonia based on the usual pneumonia test pneumonia. In addition, it was found that the closer the data distribution of the sample set was to a normal distribution, the higher the prediction accuracy was, and then, after excluding pneumonia from the test below 60 points, the prediction accuracy reached 96%.
特发性间质性肺炎（IIP）是一组不明原因的进行性下呼吸道疾病，其特征为弥漫性肺泡炎和肺泡结构紊乱，导致肺纤维化和高血压、肺心病和右心衰竭，其中一半以上死于呼吸衰竭。为了解决预测方法过于复杂和预测过程中数据集过大的问题，本文提出了一种基于高斯简约贝叶斯算法的间质性肺炎预测模型。收集了来自不同患者的三种常见肺炎测试作为样本集。这些样本被分为训练集和测试集。此外，还使用了交叉验证策略来避免过拟合问题。结果表明，基于高斯简约贝叶斯算法的预测模型在测试集上的预测准确率达到了 92%，并且简约贝叶斯方法可以根据常见的肺炎测试肺炎直接预测间质性肺炎的最终检测。此外，还发现样本集的数据分布越接近正态分布，预测准确率越高，然后排除测试中低于 60 分的肺炎，预测准确率达到 96%。
