Major depressive disorder prediction using data science

Background@#Major depressive disorder is a mood disorder that has affected many people worldwide. It is characterized by persistently low or depressed mood, anhedonia or decreased interest in pleasurable activities, feelings of guilt or worthlessness, lack of energy, poor concentration, appetite changes, psychomotor retardation or agitation, sleep disturbances, or suicidal thoughts. @*Objective@#The objective of the study was to predict the presence of major depressive disorder using a variety of machine learning classification algorithms (logistic regression, Naive Bayes, support vector machine, random forest, adaptive boosting, and extreme gradient boosting) on a publicly available depression dataset. @*Methodology@#After data pre-processing, several experiments were performed to assess the recursive feature elimination with cross validation as a feature selection method and synthetic minority over-sampling technique to address dataset imbalance. Several machine learning algorithms were applied on an anonymized publicly available depression dataset. Feature importance of the top performing models were also generated. All simulation experiments were implemented via Python 3.8 and its machine learning libraries (Scikit-learn, Keras, Tensorflow, Pandas, Matplotlib, Seaborn, NumPy). @*Results@#The top performing model was obtained by logistic regression with excellent performance metrics (91% accuracy, 93% sensitivity, 85% specificity, 93% recall, 93% F1-score, and 0.78 Matthews correlation coefficient). Feature importance scores of the most relevant attribute were also generated for the best model. @*Conclusion@#The findings suggest the utility of data science techniques powered by machine learning models to make a diagnosis of major depressive disorders with acceptable results. The potential deployment of these machine learning models in clinical practice can further enhance the diagnostic acumen of health professionals. Using data analytics and machine learning, data scientists can have a better understanding of mental health illness contributing to prompt and improved diagnosis thereby leading to the institution of early intervention and medical treatments ensuring the best quality of care for our patients.

Thyroid disorder classification using machine learning models

Introduction@#Thyroid hormones are produced by the thyroid gland and are essential for regulating the basal metabolic rate. Abnormalities in the levels of these hormones lead to two classes of thyroid diseases – hyperthyroidism and hypothyroidism. Detection and monitoring of these two general classes of thyroid diseases require accurate measurement and interpretation of thyroid function tests. The clinical utility of machine learning models to predict a class of thyroid disorders has not been fully elucidated. @*Objective@#The objective of this study is to develop machine learning models that classify the type of thyroid disorder on a publicly available thyroid disease dataset extracted from a machine learning data repository. @*Methods@#Several machine learning algorithms for classifying thyroid disorders were utilized after a series of pre-processing steps applied on the dataset. @*Results@#The best performing model was obtained by with XGBoost with a 99% accuracy and showing very good recall, precision, and F1-scores for each of the three thyroid classes. Generally, all models with the exception of Naïve Bayes did well in predicting the negative class generating over 90% in all metrics. For predicting hypothyroidism, XGBoost, decision tree and random forest obtained the most superior performance with metric values ranging from 96-100%. On the other end in predicting hyperthyroidism, all models have lower classification performance as compared to the negative and hypothyroid classes Needless to say, XGBoost and random forest did obtain good metric values ranging from 71-89% in predicting hyperthyroid class. @*Conclusion@#The findings of this study were encouraging and had generated useful insights in the application and development of faster automated models with high reliability which can be of use to clinicians in the assessment of thyroid diseases. The early and prompt clinical assessment coupled with the integration of these machine learning models in practice can be used to determine prompt and precise diagnosis and to formulate personalized treatment options to ensure the best quality of care to our patients.