Decision Support System For Clinical Diagnosis Based On Emotion Detection

Abstract

Abstract newlineOne of the key aspects of Artificial Intelligence (AI) and a persistent prerequisite of newlinehealthcare is to evaluate complicated circumstances, make forecasts, and determine newlinepatterns. Computerized clinical decision support systems (CDSS), reflects a dramatic newlinetransformation in healthcare today using machine learning and computer vision. This newlinefield of AI hopes to incorporate the human capacities of data sensing, data interpretation, newlineand behaviour based on historical and current findings into computers. An newlineevolving area that finds many therapeutic uses is the automated interpretation of newlinefacial expressions and speech analysis. newlineParkinson s disease (PD) is a movement disorder that impacts the neurological system. newlineFacial bradykinesia is a significant motor symptom of PD that results in the newlinereduction and the slowdown of facial movements of patients. Often dysarthria is observed newlinein patients with PD which causes slurred or slow speech that can be difficult newlineto understand. newlineSince there are currently no proven biomarkers for diagnostic tests, conventional PD newlinediagnosis methodologies are primarily on a patient s clinical history and physical newlineexamination. Time-consuming assessments are carried out by qualified medical professionals. newlineIt can become burdensome if periodic re-evaluation is necessary. newlineDevelopment of tools and techniques using computer vision and machine analysis may newlineturn into an alternative automated evaluation in verbal and non-verbal platforms in newlinethis scenario. This leads to the motive of this research work as to develop automated newlineDecision Support System (DSS) in PD management. Such DSS has the potential to newlineimprove healthcare by bridging the gap between optimal practice and actual clinical newlinecare. newlineA major challenge in the implementation of this research work is the unavailability of newlinethe data (facial expressions images and raw speech data) of patients with PD due to newlineethical constraints. Thus, in the initial phase of implementation, the proposed DSS newlinemodules were trained and tested using data from freel