Application of deep and machine learning techniques for multi-label classification performance on psychotic disorder diseases

Electronic Health Records (EHRs) hold symptoms of many diverse diseases and it is imperative to build models to recognise these problems early and classify the diseases appropriately. This classification task could be presented as a single or multi-label problem. Thus, this study presents Psychotic Disorder Diseases (PDD) dataset with five labels: bipolar disorder, vascular dementia, attention-deficit/hyperactivity disorder (ADHD), insomnia, and schizophrenia as a multi-label classification problem. The study also investigates the use of deep neural network and machine learning techniques such as multilayer perceptron (MLP), support vector machine (SVM), random forest (RF) and Decision tree (DT), for identifying hidden patterns in patients' data. The study furthermore investigates the symptoms associated with certain types of psychotic diseases and addresses class imbalance from a multi-label classification perspective. The performances of these models were assessed and compared based on an accuracy metric. The result obtained revealed that deep neural network gave a superior performance of 75.17% with class imbalance accuracy, while the MLP model accuracy is 58.44%. Conversely, the best performance in the machine learning techniques was exhibited by the random forest model, using the dataset without class imbalance and its result, compared with deep learning techniques, is 64.1% and 55.87%, respectively. It was also observed that patient's age is the most contributing feature to the performance of the model while divorce is the least. Likewise, the study reveals that there is a high tendency for a patient with bipolar disorder to have insomnia; these diseases are strongly correlated with an R-value of 0.98. Our concluding remark shows that applying the deep and machine learning model to PDD dataset not only offers improved clinical classification of the diseases but also provides a framework for augmenting clinical decision systems by eliminating the class imbalance and unravelling the attributes that influence PDD in patients.