Healthcare Prognosis Using Machine Learning: A Review

  Healthcare Prognosis Using Machine Learning: A Review

1 Miss. Dhanashri Belsare, 2 Dr. G.R. Bamnote

1 PG Scholar, 2 Professor

1, 2 Computer Science & Engineering

1, 2 Prof. Ram Meghe Institute of Technology & Research, Badnera, Amravati, Maharashtra, INDIA

Abstract:

Diseases tracing plays important role in daily life. Everyone cares about their health. According to some social study, a lot of people spends their time online searching for health-related issues. By browsing they get a lot of information about medical concepts and health-related issues. Normally, people use Google to search their queries and that search engine responds to them with the answer but that answer is in scattered format. User does not get the exact answer to their queries. From previous work, there have been vital work on the information needs of health seekers in terms of questions and then select those that ask for a possible disease of their manifested symptoms for further analysis. To resolve such issues an extensive experiment on a real-world dataset labeled by online doctors show significant performance.

This paper discussed the techniques for further restructuring of the question and the answer has been done to get the exact answer of the query. A tag mining framework for health seekers will be proposed; aim to identify discriminant features for each specific disease.

In this paper, going to use one of the most famous algorithms of machine learning that is the 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, split the population into two or more homogeneous sets. This is done based on the most significant attributes independent variables to make as distinct groups as possible.

Keywords: SVM (Support Vector Machine), sparse deep learning, Classifiers, Querying.