Disease Prediction using Machine Learning Techniques in Healthcare

Rashmi V. Shinde

Big data analytics, Machine Learning, Disease prediction, Healthcare.

Abstract: In recent days big data is one of the fastest and widely used approach in each and every field. By taking the help of huge amount of data biomedical and health care areas reaches their progress and also this huge amount of data profit a perfect medical data investigation, quick disease forecasting, correct data about patient can be confidentially stored and used for predicting the disease. Furthermore the correctness of an analysis can be reduced because the number of reason like imperfect medical data, some area wise disease features which can be outbreaks the prediction. In this paper we can use a various machine learning based approach for the correct disease prediction for such prediction we can gather the hospital related data of a specific area. For imperfect data the Stochastic gradient decent method is use to accomplish the incompleteness of data. For predicting disease, in the earlier days Unimodal Disease Risk Prediction approach of CNN (CNN-UDRP) is applicable. But there are some limitation for CNN-UDRP as it consider only labelled or structure data so to overcome the limitation of CNN-UDRP approach we concentrate on other CNN-MDRP approach as it works on both labeled and unlabeled type of data. Still now the existing systems are not feasible for working with different type of data that’s why the CNN-MDRP approach is more suitable for predicting the diseases with respect to other approaches.

Volume 4 Issue 9, September-2019

Pages : 1 - 5