Prediction of Kidney Disease Progression Using K-Means Algorithm Approach on Histopathology Data

Early diagnosis and prediction of disease progression are crucial for effective medical intervention. This paper presents the employability the K-Means algorithm for predicting kidney disease progression as a global health concern, using histopathological data. We conducted an analysis of histopathological data, which includes images of kidney tissue obtained through histological examination, from patients diagnosed with kidney disease. The K-Means algorithm was employed to cluster this data into homogeneous groups based on structural and cellular characteristics, subsequently predicting disease progression for each group. These predictions can aid physicians in recognizing disease progression patterns, thereby facilitating informed decisions regarding patient management and care. Our findings suggest that the K-Means algorithm can accurately predict kidney disease progression using histopathological data.