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@article{152543,
        author = {Munish Goswami and Anupama Sharma and Harshit Gupta and Sachin Wakurdekar},
        title = {CHRONIC KIDNEY DISEASE DETECTION USING RFA },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {3},
        pages = {714-719},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152543},
        abstract = {Timely Diagnosis in healthcare is very crucial and important. According to the World Health Organization (WHO) at least 8.8 million people die of cancer annually due to late diagnosis. In the present times and Technology, we can predict a diagnosis given the Important Features (example, red blood cell count, white blood cell count, etc). We can predict the diagnosis only after Analysis of Large Data Set of people having and not having a Disease. Currently, kidney disease is a major problem. Because there are so many people with this disease. The objective is to develop a simple yet powerful tool, Chronic Kidney Disease Predictor, which predicts whether a Person has CKD or not (through a Data Set of 400 people) and provides a prediction which can be confirmed by performing a KFT, Medical test. With the help of Machine Learning and use of several Algorithms like Random Forest, SVM etc we can be able to Predict whether a Patient has C.K.D. or not. Users can be a doctor or any person who is able to read Medical Reports and can send the result to the Patient in just one click. It is especially very useful for people in health care because of time diagnosis.},
        keywords = {ML- Machine Learning, WHO- World Health Organization, CKD- Chronic Kidney Disease, SVM- Support Vector Machine, KFT- Kidney Function Test,   LR- Logistic Regression, KNN- K Nearest Neighbours.},
        month = {},
        }