The Importance of Early Detection and Risk Assessment for Chronic Disease Based on IF-ORBF-DA Approach

While accurate epidemiological data is lacking, the suggested method takes into account the fact that a sizable percentage of the population is dealing with chronic kidney disease (CKD) at an early, manageable stage. Worldwide, renal replacement treatment (RRT) is becoming more popular. Chronic kidney disease (CKD) is becoming an increasingly serious clinical concern and a financial and organisational risk because to the high cost of RRT. This is the sequential order of the steps: preprocessing, feature selection, and model training. Improving the data quality relies heavily on the preprocessing step, which fixes critical issues including data encoding, missing values, and outliers. The purpose of the feature selection process is to extract the most relevant features from the initial dataset and discard the rest that aren't relevant to the job at hand. We trained this model using the IF-ORBF-DA method. It looks like this strategy is outdated compared to IF and ORBF. An astounding 96.24% accuracy percentage, clearly an improvement, as shown by the data.