• AFROZ FATIMA

The Advanced Healthcare System applies Machine Learning Algorithms for the implementation and development of medical facilities, enabling precise decisions about patient treatment and diagnosis. This System helps people to process and analyze, generate deep insights of large datasets of medical procedures. Physicians could use these advanced application, further in the provision of medical care. Therefore the significant contribution towards Machine Learning applications in healthcare could improve patient satisfaction. In this paper, we are attempting to incorporate Healthcare- Machine learning functionalities inside a single program. This sophisticated technology of machine leaning has made the healthcare system intelligent such that instead of diagnosis, the disease could be predicted using predictive algorithms of machine learning. Some cases can occur when diagnosing a disease early is not within reach. It is also possible to successfully apply the prediction of disease. As widely stated "Prevention is better than cure," disease prediction and epidemic outbreak would lead to an early prevention of a disease occurrence. This paper focuses mainly on the implementation of a program or we might claim an immediate medical service that would integrate the symptoms gathered from multisensory tools and other medical data and store them in a healthcare dataset. This dataset would then be analyzed using Decision tree, Random Forest, NaïveBayes machine learning algorithms to ensure maximum accuracy of results.

Decision tree, Random Forest, NaïveBayes, Machine Learning,

"DISEASE PREDICTION USING MACHINE LEARNING OVER BIGDATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 10, page no.338-342, October-2020, Available :http://www.jetir.org/papers/JETIR2010043.pdf

"DISEASE PREDICTION USING MACHINE LEARNING OVER BIGDATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 10, page no. pp338-342, October-2020, Available at : http://www.jetir.org/papers/JETIR2010043.pdf