Multi-class classification model for psychiatric disorder discrimination.

BACKGROUND: Physicians follow-up a symptom-based approach in the diagnosis of psychiatric diseases. According to this approach, a process based on internationally valid diagnostic tools such as The Diagnostic and Statistical Manual of Mental Disorders (DSM) or International Classification of Diseases (ICD), patient reports and the observation and experience of the physician is monitored. As in other fields of medicine, the search for biomarkers that can be used in processes related to diseases continues in psychiatry and various researches are carried out in this field. OBJECTIVES: Within the scope of this study, a dataset containing electroencephalogram (EEG) measurements of individuals diagnosed with different psychiatric diseases were analyzed by machine learning methods and the diseases were differentiated/classified with the models obtained. Thus, it was investigated whether EEG data could be a biomarker for psychiatric diseases. MATERIALS AND METHODS: In the dataset analyzed within the scope of the study, for 550 patients (81 bipolar disorder, 95 attention deficit and hyperactivity disorder - ADHD, 67 depression, 34 obsessive compulsive disorder - OCD, 75 opioid, 146 posttraumatic stress disorder - PTSD, 52 schizophrenia) and 84 healthy individuals, there are 634 samples (rows), 77 variables (columns) in total. 76 of the variables consist of absolute power values belonging to 4 frequency bands (alpha, beta, delta, theta) collected from 19 different electrodes. 80 % of the dataset was used for training the models and 20 % of the data was used for testing the performance of the models. The 5-fold cross validation (CV) method, which repeats 3 times in the training dataset, was used and with this method, the hyperparameters used in the models were also optimized. Different models have been established with the selected hyperparameters and the performance of these models has been tested with the test dataset. C5.0, random forest (RF), support vector machine (SVM) and artificial neural networks (ANN) were used to build the models. RESULTS: Within the scope of the study, the absolute power values obtained from EEG measurements performed using 19 electrodes were analyzed by machine learning methods. It was concluded that classification between disease groups was feasible with a high accuracy (C5.0-0.841, SVM_radial - 0.841, RF - 0.762). It was observed that ADHD, depression and schizophrenia diseases can be differentiated better (F-score = 1, balanced accuracy = 1) once the results were evaluated on a class category basis according to the F- measure and balanced accuracy values. DISCUSSION AND CONCLUSION: Through the medium of the analyzes made within the scope of this study, it was investigated whether EEG data could be used as a biomarker for the detection and diagnosis of psychiatric diseases. The findings obtained from this study revealed that by using EEG data as a biomarker, it can be highly predicted whether a person has a psychiatric disease or not. Once evaluated with broad strokes, it is feasible to assert that it is possible to analyze whether the person who consults a physician with a complaint is ranked among the psychiatric disease class with EEG measurement. When trying to differentiate between numerous and diverse disease categories, it may be claimed that some diseases (ADHD, depression, schizophrenia) can be distinguished better by coming to the fore on a model basis. Considering the findings, it is anticipated that the analyzes obtained as a result of this study will contribute to the studies to be conducted using machine learning in the field of psychiatry.

The analysis of the effects of acute rheumatic fever in childhood on cardiac disease with data mining.

BACKGROUND: Acute rheumatic fever (ARF) is an important disease that is frequently seen in Turkey, it is necessary to develop solutions to cure the disease. It is believed that new data analysis methods may be applied to this disease, and this may be useful to discover previously unrecognized patterns. Data mining of existing records and data repositories may improve knowledge on the diagnosis and management of ARF. In this regard, we planned to make a contribution to the development of new solutions by approaching the problem from a different standpoint. OBJECTIVES: The aim of this study is to analyse the effects of ARF undergone during childhood on the basis of cardiac diseases by using data mining methods. MATERIALS AND METHODS: Classification methods of data mining were used, and experiments were conducted on five algorithms. The records of the patients diagnosed with ARF were analysed by setting models with naive Bayes classifier, decision trees (CART, C4.5, C5.0, C5.0 boosted) and random forest algorithms. The performances of the algorithms that were derived were then compared. Among model performance evaluation techniques, the hold-out, cross-validation and bootstrap methods were tested in diverse ways in an applied manner. Within the scope of the research, the dataset comprising records of 297 patients was utilised in cooperation with Istanbul Medeniyet University Göztepe Training and Research Hospital's Pediatric Cardiology Clinic (Istanbul Medeniyet Üniversitesi Göztepe Egitim ve Arastirma Hastanesi Çocuk Kardiyolojisi Klinigi). Data analysis was carried out with the data of the remaining 201 patients following pre-processing. RESULTS: The results that were obtained from different algorithms were compared based on the model performance evaluation criteria. The best result was shown under the CART model by using the hold-out technique (80% training, 20% testing). According to this model, the importance values of the predictive attributes were listed, and it was found that the "teleNormal" and "cardiomegaly" attributes were not required for ARF diagnosis and treatment. In compliance with this result, it was thought that it should not be necessary for patients have a chest x-ray which is needed for diagnosis of "teleNormal" and "cardiomegaly". This will help reduce costs and thus contribute to the health economy while preventing patients from having unnecessary x-rays. DISCUSSION AND CONCLUSION: The results of this study showed that data mining techniques may be used to analyse diseases such as ARF. The important attributes that affect the disease were obtained in accordance with the results. The results of the best model (CART) may be broadened in numerous ways and provide information for both experienced and inexperienced physicians. This study is considered to be significant as it helps data mining methods become more prevalently used for data analysis in fields of medicine and healthcare.