Published Paper Details:

The Chronic Kidney Disease (CKD) is a major health problem around the world. There are several reasons for this and it results in some serious health issues like renal failure, cardiovascular diseases and ultimately leads to early death. Recent study shows that the CKD has increased around 17% worldwide. It is found that out of 10 people 1 is affected by CKD. The early prediction of this disease helps in control through medicines. The aim of this project is to apply machine learning algorithms in the early prediction and also to find the major factor behind this. The dataset is publicly available at Kaggle, it has 400 rows and 26 features. The dataset is preprocessed (like removing null values, duplicate values, etc.) to make the dataset to be used to build the model. Logistic Regression, Decision Tree classifier and K-Nearest Neighbor algorithms are applied in the dataset to obtain the best fit model. The Logistic Regression outperforms will with the other algorithms and produces the accuracy of 99%.