@Article{iasc.2024.041535,
AUTHOR = {Ghulam Gilanie, Sana Cheema, Akkasha Latif, Anum Saher, Muhammad Ahsan, Hafeez Ullah, Diya Oommen},
TITLE = {A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {39},
YEAR = {2024},
NUMBER = {1},
PAGES = {57--71},
URL = {http://www.techscience.com/iasc/v39n1/55869},
ISSN = {2326-005X},
ABSTRACT = {Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient. It affects a large percentage of people globally, who fluctuate between depression and mania, or vice versa. A pleasant or unpleasant mood is more than a reflection of a state of mind. Normally, it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio, so automated procedures are the best options to diagnose and verify the severity of bipolar. In this research work, facial micro-expressions have been used for bipolar detection using the proposed Convolutional Neural Network (CNN)-based model. Facial Action Coding System (FACS) is used to extract micro-expressions called Action Units (AUs) connected with sad, happy, and angry emotions. Experiments have been conducted on a dataset collected from Bahawal Victoria Hospital, Bahawalpur, Pakistan, Using the Patient Health Questionnaire-15 (PHQ-15) to infer a patient’s mental state. The experimental results showed a validation accuracy of 98.99% for the proposed CNN model while classification through extracted features Using Support Vector Machines (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT) obtained 99.9%, 98.7%, and 98.9% accuracy, respectively. Overall, the outcomes demonstrated the stated method’s superiority over the current best practices.},
DOI = {10.32604/iasc.2024.041535}
}