Disease Identification from Unstructured User Input

Faisal, Fahim; Bhuiyan, Shafkat Ahmed

dc.contributor.author	Faisal, Fahim
dc.contributor.author	Bhuiyan, Shafkat Ahmed
dc.date.accessioned	2017-10-25T09:57:17Z
dc.date.available	2017-10-25T09:57:17Z
dc.date.issued	2016-11-20
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dc.identifier.uri	http://hdl.handle.net/123456789/95
dc.description	Supervised by Dr. Abu Raihan Mostofa Kamal Associate Professor Department of Computer Science and Engineering Islamic University of Technology (IUT	en_US
dc.description.abstract	In this information age the number of internet users are growing rapidly. Now a days people rst search internet if they face any health hazard rather than asking a doctor for health related advice as online medical help or health care advice is easier to grasp. Sometimes, people give less importance to minor symptoms which may cause serious health hazards. In this context, online health advice can be instant bene ciary. Moreover, existing online symptom checkers give possible sense of disease but these systems are not reliable enough. Also existing systems are not interactive and time consuming. Herein, we propose an automated disease identi cation system that takes unstructured user input and provides a list (topmost diseases that have greater likelihood of occurrence) of probable diseases. We use Conditional Random Field and Support Vector Machine to detect the word phrases and to classify the class labels.By not considering demographic information, it gives 4.603% accuracy improvement whereas, considering demographic information we get slightly better performance with 5.783% accuracy improvement.	en_US
dc.language.iso	en	en_US
dc.publisher	IUT, CSE	en_US
dc.title	Disease Identification from Unstructured User Input	en_US
dc.type	Thesis	en_US