MULTI-MODAL DATA ANALYSIS FOR STROKE PREDICTION: UNVEILING HIDDEN BIOMARKERS THROUGH MACHINE LEARNING

Subject Area: ARTIFICIAL INTELLIGENCE: MACHINE LEARNING FOR GENDER DISPARITY RESOLUTION

Stroke, a leading cause of disability and mortality worldwide, presents a critical challenge for healthcare systems. While demographic factors have traditionally guided stroke risk assessment, recent advancements in machine learning and multimodal data analysis offer promising avenues for uncovering hidden biomarkers. In this project, we propose a novel approach that transcends conventional demographic-based models by integrating diverse data modalities, including genetic, imaging, clinical, and lifestyle factors. The multifaceted nature of stroke demands a comprehensive understanding of its underlying mechanisms, which extend beyond simple demographic variables. By harnessing the power of multimodal analysis, our methodology aims to unveil intricate patterns and interactions among these diverse data sources. Through sophisticated machine learning algorithms, we seek to identify subtle yet significant relationships between genetic predispositions, imaging biomarkers, clinical parameters, and lifestyle habits, collectively contributing to stroke risk. Central to our approach is the recognition that stroke is a complex, multifactorial disease influenced by a myriad of interconnected factors. Conventional models often overlook this complexity, relying solely on demographic characteristics such as age, sex, and ethnicity. In contrast, our methodology embraces the richness of multimodal data, enabling the discovery of novel biomarkers that may have been previously obscured. Furthermore, our research extends beyond mere prediction by aiming to elucidate the underlying biological mechanisms driving stroke susceptibility. By unraveling these hidden biomarkers, we can not only enhance the accuracy of predictive models but also gain insights into the pathophysiology of stroke, thus paving the way for more targeted interventions and personalized treatment strategies.