利用计算智能技术对精神分裂症进行先发制人的诊断

Mona M. Almutairi, Nada Alhamad, Albandari Alyami, Zainab Alshobbar, H. Alfayez, Noor Al-Akkas, Jamal Alhiyafi, S. Olatunji
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引用次数: 7

摘要

精神分裂症是一种严重的慢性精神障碍,影响患者的行为、感知和思维。本研究的目的是开发一种预测系统,使用基于计算智能的技术预先诊断精神分裂症疾病。该系统将在早期阶段显示患病的可能性,这将改善患者的健康状况。这将使用机器学习技术来完成。使用的数据集有86条记录,这些记录来自于信号处理机器学习(MLSP) 2014年精神分裂症分类Kaggle挑战。本文使用的技术包括支持向量机(SVM)、随机森林(RF)、人工神经网络(ANN)和朴素贝叶斯(NB)。SVM、RF、NB技术准确率最高,达到90.6977%,ANN技术准确率最高,达到88.3721%。通过使用204个特征来达到精度。因此,我们得出结论,在这个特定的问题中,使用SVM、RF和NB技术更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preemptive Diagnosis of Schizophrenia Disease Using Computational Intelligence Techniques
Schizophrenia is a severe chronic mental disorder, which affects the behavior, the perception and the thinking of the patient. The purpose of this research is to develop a predictive system to preemptively diagnose Schizophrenia Disease using computational intelligence-based techniques. The system will show the possibilities of getting the disease at an early stage, which will improve the health state of the patients. This will be done using machine learning techniques. The used dataset has 86 records, which was obtained from the Machine Learning for Signal Processing (MLSP) 2014 Schizophrenia Classification Kaggle Challenge. The used techniques in this paper are Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Naive Bayesian (NB). The highest accuracy was 90.6977% reached by using SVM, RF, and NB techniques while ANN technique reached 88.3721% accuracy. The obtained accuracies are reached by using 204 features. Therefore, we conclude that using SVM, RF, and NB techniques are better in this particular problem.
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