Review Paper on a Healthcare Prognosis Using Machine Learning

Abstract

Abstract: Diseases tracing plays important role in daily life. Every one cares about their own health. According to some social study, lot of people spends their time on online searching of health related issues. By browsing they get lot of information about the medical concepts and health related issues. Normally, people use Google to search their queries and that search engine respond them with the answer but that answer is in scattered format. User does not gets the exact answer for their queries. From previous work there has been vital work on the information needs of health seekers in terms of questions and then select those that ask for possible disease of their manifested symptoms for further analytic. To resolve such issues an extensive experiment on a real-world dataset labelled by online doctor’s show the significant performance. In this paper, we discussed the techniques for further restructuring of the question and answer has been done in order to get the exact answer of query. A tag mining framework for health seekers will be proposed; aim to identify discriminant features for each specific disease. In this paper we are going to use one of the most famous algorithm of machine learning that is decision tree. It is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible. Keywords: SVM (Support Vector Machine), sparse deep learning, Classifiers, Querying, Signature mining