Proactive healthcare: machine learning-driven insights into kidney failure prediction

Kidney failure, a critical condition with increasing prevalence, necessitates early detection and management to mitigate its severe health impacts. In this study, we utilize the MIMIC-IV dataset to develop predictive models for identifying and forecasting kidney failure using advanced machine learning techniques. We aggregated medical records from patients diagnosed with kidney failure, alongside an equivalent dataset from non-kidney failure individuals, to train LSTM, random forest, and XGBoost models. Comprehensive data analysis was conducted to extract and evaluate key features related to kidney function, including correlations among lab events, prescriptions, and patient demographics. These insights informed the model development, enabling accurate classification of kidney failure based on historical medical data and prediction of its onset through time-series analysis. The Random Forest and XGBoost models outperformed others, achieving near-perfect accuracy, demonstrating their robustness in handling complex medical datasets. Additionally, we conducted feature prediction, forecasting critical lab event values for patients with kidney failure, which can inform early interventions and personalized treatment plans. Our findings underscore the potential of machine learning in enhancing clinical decision-making, offering a pathway to more precise and proactive healthcare strategies in managing kidney failure.