<![CDATA[Longitudinal health data analysis helps diagnose and treat disease. Traditional deterministic models fail to represent longitudinal data's unpredictability and uncertainty, limiting their forecast accuracy and decision-making capacities. This research improves Longitudinal Health Data Analysis by adding stochastic models for disease trajectories and therapy optimization. The research begins with a stochastic model that accounts for the complicated dynamics of illness progression and therapy responses. This model captures individual variability and probability outcomes using patient-specific factors, features, and treatment information. Numerical examples demonstrate the model's practicality. The numerical example shows that the stochastic model may forecast illness trajectories and optimize treatment choices. The model predicts illness development probabilistically, helping understand disease dynamics and identify high-risk patients. Simulating and probabilistically estimating therapeutic interventions optimizes treatment options. Personalized therapy decision-making improves patient outcomes. Longitudinal Health Data Analysis should use stochastic models, the study suggests. These models improve disease prediction, therapy optimization, and personalized healthcare decision-making by capturing variability and uncertainty. Advanced modeling methodologies and real-world data validation are next. The research could change illness management and clinical care]]>
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