Preemptive Diagnosis of Schizophrenia Disease Using Computational Intelligence Techniques

Almutairi, Mona; Alhamad, Nada; Alyami, Albandari; Alshobbar, Zainab; Alfayez, Heela; Al-Akkas, Noor; Alhiyafi, Jamal; Olatunji, Sunday O.

doi:10.1109/cais.2019.8769513

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…In the study [ 37 ], the authors with a sample of 20 features obtained an average classification accuracy of the combined feature set of 78.2% using SVM. In [ 36 ] with a sample of 204/410 features, the authors obtained accuracy values of 86.04–90.69% using SVM, ANN, Random Forest, and Naïve Bayes algorithms. By reducing the number of features to 11, the values decrease to 82.55–83.72%.…”

Section: Discussionmentioning

confidence: 99%

“…The authors of [ 50 ] have made an initial review of this research topic. Moreover, by performing a more updated analysis of the literature, the authors find relevant studies of schizophrenia [ 27 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ] that focus their research on these machine learning techniques.…”

Section: Methodsmentioning

confidence: 99%

“…In studies such as [ 36 ], the authors analyzed a predictive system to preventively diagnose schizophrenia disease using SVM, Random Forest, ANN, and Naïve Bayes. The results showed the highest precision of 90.69% using SVM, Random Forest, and Naïve Bayes.…”

Section: Introductionmentioning

confidence: 99%

“…The authors have tested the previously mentioned low computational machine learning methods since several researchers have used the same methods and it is critical to compare the performance of the methods to understand if the results converge in this field of research. The authors expect Random Forest to provide the best performance based on the previous research activities of other authors [ 35 , 36 , 37 , 38 , 39 ]. These studies are different regarding the number of features, sample population, and algorithms they use to develop their research.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia

Alonso

Marques

Agarwal

et al. 2022

Sensors

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New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia

Alonso

Marques

Agarwal

et al. 2022

Sensors

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“…Researchers in reference [9] obtained a dataset from King Abdulaziz University (KAU) Hospital, Saudi Arabia, to diagnose AD earlier using cerebral catheter angiogram neuroimaging, achieving an accuracy of 99.14%. Furthermore, researchers in references [10][11][12][13] utilized various ML techniques to perform a pre-emptive diagnosis of diabetes mellitus, chronic kidney disease, schizophrenia, and thyroid cancer, attaining the highest accuracies of 98.00%, 98.00%, 90.70%, and 90.91%, respectively. e promising results of these studies have encouraged us to consider expanding the work to build a prediction model for AD using simple clinical and demographical data.…”

Section: Introductionmentioning

confidence: 99%

Preemptive Diagnosis of Alzheimer’s Disease in the Eastern Province of Saudi Arabia Using Computational Intelligence Techniques

Olatunji

Alansari

Alkhorasani

et al. 2022

Computational Intelligence and Neuroscience

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Alzheimer’s Disease (AD) is a silent disease that causes the brain cells to die progressively, influencing consciousness, behavior, planning ability, and language to name a few. AD increases exponentially with aging, where it doubles every 5-6 years, causing profound implications, such as swallowing difficulties and losing the ability to speak before death. According to the Ministry of Health in Saudi Arabia, AD patients will triple by 2060 to reach 14 million patients worldwide. The rapid rise of patients is caused by the silent progress of the disease, leading to late diagnosis as the symptoms will not be distinguished from normal aging affect. Moreover, with the current medical capabilities, it is impossible to confirm AD with 100% certainty via specific medical examinations. The literature review revealed that most recent publications used images to diagnose AD, which is insufficient for local hospitals with limited imaging capabilities. Other studies that used clinical and demographical data failed to achieve adequate results. Consequently, this study aims to preemptively predict AD in Saudi Arabia by employing machine learning (ML) techniques. The dataset was acquired from King Fahad Specialist Hospital (KFSH) in Dammam, Saudi Arabia, containing standard clinical tests for 152 patients. Four ML algorithms, namely, support vector machine (SVM), k-nearest neighbors (k-NN), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost), were employed to preemptively diagnose the disease. The empirical results demonstrated the robustness of SVM in the pre-emptive diagnosis of AD with accuracy, precision, recall, and area under the receiver operating characteristics (AUROC) of 95.56%, 94.70%, 97.78%, and 0.97, respectively, with 13 features after applying the sequential forward feature selection technique. This model can assist the medical staff in controlling the progression of the disease at low costs.

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Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023

Jafari,

Sadeghi,

Shoeibi

et al. 2023

Appl Intell

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Preemptive Diagnosis of Schizophrenia Disease Using Computational Intelligence Techniques

Cited by 6 publications

References 9 publications

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia

Preemptive Diagnosis of Alzheimer’s Disease in the Eastern Province of Saudi Arabia Using Computational Intelligence Techniques

Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023

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