Mona M. Almutairi, Nada Alhamad, Albandari Alyami, Zainab Alshobbar, H. Alfayez, Noor Al-Akkas, Jamal Alhiyafi, S. Olatunji
{"title":"利用计算智能技术对精神分裂症进行先发制人的诊断","authors":"Mona M. Almutairi, Nada Alhamad, Albandari Alyami, Zainab Alshobbar, H. Alfayez, Noor Al-Akkas, Jamal Alhiyafi, S. Olatunji","doi":"10.1109/CAIS.2019.8769513","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":220129,"journal":{"name":"2019 2nd International Conference on Computer Applications & Information Security (ICCAIS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Preemptive Diagnosis of Schizophrenia Disease Using Computational Intelligence Techniques\",\"authors\":\"Mona M. Almutairi, Nada Alhamad, Albandari Alyami, Zainab Alshobbar, H. Alfayez, Noor Al-Akkas, Jamal Alhiyafi, S. Olatunji\",\"doi\":\"10.1109/CAIS.2019.8769513\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":220129,\"journal\":{\"name\":\"2019 2nd International Conference on Computer Applications & Information Security (ICCAIS)\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 2nd International Conference on Computer Applications & Information Security (ICCAIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAIS.2019.8769513\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 2nd International Conference on Computer Applications & Information Security (ICCAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAIS.2019.8769513","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.