Prediction of Renal Illness using Machine Learning Models - Sanaboina, Chandra Sekhar; KURELLA, SRI SATYA PREM CHARAN (INFOCOMP Journal of Computer Science, 2023)
The biggest issue that the entire globe may encounter at this time is chronic kidney disease. Early stages are symptomless and only become apparent when kidney function has been reduced by up to 25%. Therefore, it is necessary to anticipate and detect chronic renal disease. Due to their rapid and precise detection capabilities, machine learning models are employed nearly exclusively in clinical and medical settings to identify a variety of chronic conditions. Here Chronic kidney Disease dataset is used from the UCI repository, several machine-learning algorithms are used in order to predict various chronic diseases. The proposed system uses a Stochastic Gradient Descent algorithm to make our model learn a lot faster. The expected results will be a comparative table for various machine learning algorithms with respect to performance metrics like Precision, F1-score, Recall, and Accuracy.