Formulation of a Multi-Disease Comorbidity Prediction Framework: A Data-Driven Case Analysis on of Diabetes, Hypertension, and Cardiovascular Risk Trajectories

Abstract

The development of a multi-disease comorbidity prediction framework is the main drive point of this study, leading to the data-driven case analysis of diabetes, hypertension, and cardiovascular risk trajectories. As non-communicable diseases are showing more and more overlapping patterns, the study is able to predict how to create that model by taking into consideration the co-occurrence instead of treating diseases in a separate way. Using a well-organized medical dataset that holds demographic and clinical factors that include age, gender, BMI, smoking history, blood glucose, and HbA1c levels, the study uses linear regression methods to evaluate risk factors of individual and cumulative disease impacts. The deployed methodology intertwines the strong preprocessing procedures, correlation analysis, and training of the model, and final overall visualization. The data visualization tools like Python, Tableau, and Microsoft Excel may also be employed to assist in obtaining insights, revealing the trends, and presenting the findings. The major trends diagnosed are the increased risk of co-morbidity in aged people, increases in BMI and level of HbA1c, the exacerbating effect of a smoking history. These results point to the interdependence of metabolic health and cardiovascular health and indicate the need of advanced predictive pathways in clinical practice. This study also shows that visual analytics in tandem with explanatory statistics modeling can be very relevant when diagnosing cases at the early stage and preventive health measures. This study is meaningful to producing a feasible and translatable methodology of precision public health by placing an emphasis on establishing transparency, scalability, and ease of implementation. The suggested model is a kind of decision aid which can help medical workers of the sphere to actively diagnose people at risk and provide specific interventions even before the disease could become difficult to treat or impossible to reverse.