shrishtinigam/chronic-kidney-disease-prediction

Using ML and DL algorithms to predict Chronic Kidney Disease and making a comparative study of different algorithms.

Chronic Kidney Disease Prediction

Disease prediction is the premier focus of most emerging medical technologies. Chronic kidney disease prediction is a complex task depending on two variables – the estimated glomerular filtration rate (G score) and the albumin to creatinine ratio (A score). Using machine learning techniques such as Gaussian Naive Bayes, and Logistic Regression, and deep learning techniques such as CNN Sequential model, the presented work predicts the likelihood of CKD and classifies the patient in a binary manner. The dataset is preprocessed by 4 four different methods. This research conducts a relative analysis of the performance of several machine learning and deep learning methods.

Authors - P Leena Reddy @p-leena-reddy-111 , Meher Shrishti Nigam @shrishtinigam

Current Draft of the Report - https://docs.google.com/document/d/1o8duxxORyuUx0FDG8bEjfmH8H3Kn4soi/edit?usp=sharing&ouid=102061234540277159333&rtpof=true&sd=true