Chronic kidney disease (ckd) machine learning prediction system, methods, and apparatus

A system for estimating a patient's chronic kidney disease (“CKD”) progression, the system comprising:

• a memory device storing patient characteristic data for a patient undergoing analysis, the patient characteristic data including demographic/physiological data, a CKD entry stage, a diagnosed cause of CKD, and a health history;
• an ensemble machine learning algorithm configured to predict a progression to a next stage of CKD and a timeframe of the progression of the next stage of CKD, the ensemble machine learning algorithm containing prediction decile classifiers that each includes percentages of known patients that progressed from one moderate CKD stage to a next moderate or severe CKD stage for discrete timeframes;
• and
• an analytics processor communicatively coupled to the memory device, the analytics processor in conjunction with the ensemble machine learning algorithm configured to:
• classify the patient undergoing analysis into a closest matching prediction decile for the CKD entry stage of the patient by comparing the patient characteristic data of the patient under analysis to classifications of patient characteristic data provided in the ensemble machine learning algorithm,
• determine a probability that the patient undergoing analysis will progress to a next moderate or severe CKD stage for each of the discrete timeframes based on the closest matching prediction decile, and
• display, via a user interface, the percentage likelihoods that the patient undergoing analysis will progress to the next moderate or severe CKD stage for the discrete timeframes.