Aim:

          To apply machine learning techniques result in improving the accuracy in the prediction of Chronic Kidney Disease.

Abstract:

       Chronic kidney disease (CKD) is the serious medical condition where the kidneys are damaged and blood cannot be filtered. In the end-stage of the disease the renal disease (CKD), the renal function is severely damaged. The starting date of kidney failure may not be known, it may not recognize as an illness of the patient because it cannot show any symptoms initially. And this chronic kidney disease is also called chronic renal failure, which has become quite a serious problem in the world where the kidneys are damaged and it has become the cause of improper function of kidney organ. To overcome this issue, this project aims to kidney disease diagnosis using machine learning approaches. This is done by comparing the accuracies of different algorithms and uses the algorithm with high accuracy for prediction. Our goal is to enhance the performance of the model by removing unnecessary and insignificant attributes from the dataset and only collecting those that are most informative and useful for the classification task. Thus the main focus of the system is to make use data analytics to predict the presence of the disease.

Synopsis:

      Attribute selection in  chronic kidney disease (CKD) from computer-aided diagnosis in machine learning approach in Low-cost-Diagnostic Screening.

Proposed System:

     Our Aim is to predict the chronic kidney disease using the machine learning algorithm. Chronic kidney disease (CKD) means your kidneys are damaged and can’t filter blood the way they should. The disease is called “chronic” because the damage to your kidneys happens slowly over a long period of time. This damage can cause wastes to build up in your body. CKD can also cause other health problems.10% of the population worldwide is affected by chronic kidney disease (CKD), and millions die each year because the doctors are unable diagnose the disease. The system is automation for predicting the CKD. We proposed KNN, Logistic Regression and Random Forest machine learning technique for kidney disease prediction of significant features. ML process starts from a pre-processing data phase followed by feature selection based on data cleaning, classification of modeling, performance evaluation, and the results with improved accuracy.

Advantage:

     Mode imputation is performed where the missing values are replaced with the most frequently occurred value of that attribute. After pre-processing, the data distribution is transformed its give high accuracy