TY - CONF
T1 - Preemptive Diagnosis of Schizophrenia Disease Using Computational Intelligence Techniques
AU - Almutairi, Mona M.
AU - Alhamad, Nada
AU - Alyami, Albandari
AU - Alshobbar, Zainab
AU - Alfayez, Heela
AU - Al-Akkas, Noor
AU - Alhiyafi, Jamal
AU - Olatunji, Sunday O.
N1 - 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.
PY - 2019/5/1
Y1 - 2019/5/1
N2 - 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.
AB - 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.
UR - https://ieeexplore.ieee.org/abstract/document/8769513
U2 - 10.1109/CAIS.2019.8769513
DO - 10.1109/CAIS.2019.8769513
M3 - Presentation
T2 - 2019 2nd International Conference on Computer Applications Information Security (ICCAIS)
Y2 - 1 May 2019
ER -