A Random Forest Model for Mental Disorders Diagnostic Systems

Abou-Warda, Horeya; Belal, Nahla A.; El-Sonbaty, Yasser; Darwish, Sherif

doi:10.1007/978-3-319-48308-5_64

Cited by 14 publications

(10 citation statements)

References 21 publications

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“…The diagnosis of mental disorders such as schizophrenia is the first step in a set of actions that are selected to save patients’ lives or improve their health [ 59 ]. People with this disorder tend to be hospitalized frequently and have high rates of disability, imposing an economic cost on a general level.…”

Section: Discussionmentioning

confidence: 99%

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%

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

Alonso

Marques

Agarwal

et al. 2022

Sensors

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“…Pisner and Schnyer explained the classification strategy of SVM well [ 25 ]. The linear support vector machine model is used in the prediction research for mental health diseases [ 26 ], sentiment analysis [ 27 ], and so on.…”

Section: Methodsmentioning

confidence: 99%

Performance Evaluation of Machine Learning Algorithm for Classification of Unintended Pregnancy among Married Women in Bangladesh

Hossain

Habib

Saleheen

et al. 2022

Journal of Healthcare Engineering

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Intended pregnancy is one of the significant indicators of women’s well-being. Globally, 74 million women become pregnant every year without planning. Unintended pregnancies account for 28% of all pregnancies among married women in Bangladesh. This study aimed to investigate the performance of six different machine learning (ML) algorithms applied to predict unintended pregnancies among married women in Bangladesh. From BDHS 2017-18, only 1129 pregnant women aged 15–49 were eligible for this study. An independent χ 2 test had performed before we considered six popular ML algorithms, such as logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), naïve Bayes (NB), and elastic net regression (ENR) to predict the unintended pregnancy. Accuracy, sensitivity, specificity, Cohen’s Kappa statistic, and area under curve (AUC) value were used as model evaluation. The bivariate analysis result showed that women aged 30–49 years, poor, not educated, and living in male-headed households had a higher percentage of unintended pregnancy. We found various performance parameters for the classification of unintended pregnancy: LR accuracy = 79.29%, LR AUC = 72.12%; RF accuracy = 77.81%, RF AUC = 72.17%; SVM accuracy = 76.92%, SVM AUC = 70.90%; KNN accuracy = 77.22%, KNN AUC = 70.27%; NB accuracy = 78%, NB AUC = 73.06%; and ENR accuracy = 77.51%, ENR AUC = 74.67%. Based on the AUC value, we can conclude that of all the ML algorithms we investigated, the ENR algorithm provides the most accurate classification for predicting unwanted pregnancy among Bangladeshi women. Our findings contribute to a better understanding of how to categorize pregnancy intentions among Bangladeshi women. As a result, the government can initiate an effective campaign to raise contraception awareness.

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“…This method tends to have good generalization and reduces overfitting as more individual trees are added, and therefore is robust to noise in datasets [24]. Thus far, there has been no study that investigates RF algorithms with neuroimaging in the diagnosis of MDD or classification of MDD patients from healthy controls [25][26][27][28][29][30]. One study conducted by Abou-Warda, H. et al ( 2016) used RF with impute missing values learner on datasets to enhance the diagnostic system of mental disorders [25].…”

Section: Machine Learning In Neuroimagingmentioning

confidence: 99%

“…As a consequence, machine learning approaches are integrated into neuroimaging to provide multivariate solutions that demonstrate greater sensitivity and more reliable predictions than univariate methods, thus enabling the development of imaging brain signatures at the individual level [17,18]. Among the most successful machine learning techniques, support vector machines (SVMs) [21][22][23], random forests (RF) [24][25][26][27][28][29][30], and deep learning (DL) [31][32][33] have become exceedingly popular for neuroimaging analysis in recent years.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Neuroimaging for Diagnosis of Major Depressive Disorder

Watanathawornwong

2022

J Stud Res

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Is machine learning an effective diagnostic tool to identify the presence of major depressive disorder in a patient? Major depressive disorder (MDD) is a common mood disorder, distinguished by prolong emotional distress and dysfunction in normal activities, yet its current diagnosis still depends on subjective interpretation of clinical interviews and self-reported symptoms. Not only are these prevalent methods susceptible to inconsistent diagnoses, the lack of clear boundaries in present diagnostic criteria to evaluate MDD also further magnifies the need for objective diagnostic tools in clinical environment. One of those methods explored by psychologists is the application of machine learning in neuroimaging techniques. Due to its analytical prowess, machine learning has become popular in psychological research to understand and discover biomarkers in illnesses. This literature review aims to evaluate the effectiveness of different machine learning models and neuroimaging in classifying MDD patients before discussing some of the major setbacks faced by researchers and the suggested solutions for overcoming these barriers to clinical application.

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A Random Forest Model for Mental Disorders Diagnostic Systems

Cited by 14 publications

References 21 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

Performance Evaluation of Machine Learning Algorithm for Classification of Unintended Pregnancy among Married Women in Bangladesh

Machine Learning in Neuroimaging for Diagnosis of Major Depressive Disorder

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