Predictive Model of Disease Prognosis in Healthcare

The Examination and modeling of a healthcare dataset to predict disease prognoses at the patient level. The dataset encompasses diverse features such as age, sex, general health, checkup history, exercise habits, smoking history, and the presence of diseases like heart disease, skin cancer, other cancers, diabetes, and arthritis. Exploratory data analysis reveals significant variations in disease distribution, emphasizing the influence of health and lifestyle factors on disease risk. Missing data underscores the need for effective imputation methods in healthcare predictions. Machine learning, particularly XGBOOST,Random Forest, Desion Tree is employed for disease prognosis prediction, employing rigorous training and validation through a train-test split strategy. Evaluation metrics include confusion matrix, ROC curve, and Precision- Recall curve, offering a comprehensive view of model performance. The study highlights the significance of multiple metrics in imbalanced classification tasks, showcasing machine learning’s potential in healthcare for patient risk assessment, healthcare planning, and clinical strategy formulation. Future work may focus on model refinement, class balancing strategies, and incorporating additional patient data for more accurate disease prognosis predictions.