Published Paper Details:

Chronic kidney disease (CKD) is a global health issue that causes a high rate of morbidity and mortality, as well as the onset of additional diseases. Because there are no clear symptoms in the early stages of CKD, people frequently miss it. Early identification of CKD allows patients to obtain timely treatment to slow the disease's progression. Due to their rapid and precise recognition capabilities, machine learning models can successfully assist doctors in achieving this goal. We propose a machine learning framework for diagnosing CKD in this paper. The CKD data set was taken from kaggle, which has a substantial number of missing values.We employ multiple machine learning methods such as DT, SVM, and DNN to analyze data from CKD patients with 21 characteristics and 400 records. The dataset is preprocessed by filling in missing data and normalizing it. To increase accuracy and save training time, the most relevant features from the dataset are chosen.Image processing and letter recognition are used to automatically input the attributes.