This research aims to diagnose schizophrenia with machine learning-based algorithms. Bayesian neural network, logistic regression, decision tree, k-nearest neighbor, and gaussian kernel classification techniques are investigated to diagnose schizophrenia with data from 125 persons. This study showed that left lateral ventricles and left globus pallidus volumes and their percentages in the brain were significantly lower than HCs in FEP patients. Using brain volumes, we were able to diagnose FEP with an accuracy of 73.6 % via logistic regression and with an accuracy of 86.4 % using the SVM kernel classifier method. Therefore, brain volumes can be used to diagnose FEP with the SVM kernel classifier method.

这项研究旨在利用基于机器学习的算法诊断精神分裂症。研究人员利用贝叶斯神经网络、逻辑回归、决策树、k 最近邻和高斯核分类技术，利用 125 人的数据诊断精神分裂症。这项研究表明，FEP 患者的左侧脑室和左侧苍白球体积及其在大脑中的百分比显着低于 HC。利用脑容量，我们能够通过逻辑回归诊断 FEP，准确度为 73.6%，使用 SVM 核分类器方法诊断 FEP 的准确度为 86.4%。因此，脑容量可以通过SVM核分类器方法来诊断FEP。