Statistical and Supervised Machine Learning Analysis on Clinical Uddanam Nephrology Dataset

Dr.T. Pandu Ranga Vital, L.V. Satyanarayana, T. Chalapathi Rao and B. Kameswara Rao

With massive growth in medical and health communities, precise study of therapeutic information benefits early detection of disease, tolerant consideration and community Services. However, the analysis of accuracy is diminished when the quality of medical data is imperfect. Besides different areas display elite features of certain regional disease, this may debilitate the expectation of disease epidemics. In this paper we streamline machine learning communities. We will experiment on a regional (Uddanam area Srikakulam Dist., A.P.) kidney disease. We propose convolution machine learning based multimodal disease threat prediction algorithms using structured and unstructured data from hospitals. The clinical Uddanam nephrology data set collected from different areas of north costal of srikakulam district from year of2016 to 2019.The most recent advances in Machine learning (ML) innovations give new viable ideal models to get start to finish taking in models from complex data. In this task, we apply algorithms and models of ML advances like Naïve Bayes, SVM and ADT are utilized to advance the human care domain and got accuracy up to 98.3%, 97.8%, 100%.

Volume 11 | 04-Special Issue

Pages: 2024-2039