SEMANTIC BASED TEXT CLASSIFICATION OF EMERGENT BRAIN TUMOUR DISEASE REPORTS USING MACHINE LEARNING TECHNIQUES
M.Vengateshwaran
, M.A.Mohamed Aslam , K.Ajithkumar , S.Vishnu Varthanan
Text classification, Medical Domain, Disease reports , SDL, VSM, TF-IDF
With the rapid growth of stored information has been enormously increasing day by day which is generally in the unstructured form and cannot be used for any processing to extract useful information. Text classification is a technique to find meaningful patterns from the available text documents. Medical domains are rich knowledge source needed to be organized efficiently and conveniently. Disease reports documents are used for gathering business intelligence and identifying key trends in technology development. The main focus of this paper is to propose a Disease reports document classification framework based on Semantic Deep Learner (SDL). In this framework, initially key terms of the Disease reports documents are extracted and represented using Vector Space Model(VSM), the importance of the key terms are weighted based up on their frequencies using TF-IDF. The semantic similarity between the key features are computed using cosine measure. Terms with higher correlations are synthesized into a smaller set of features. Finally the Semantic Deep Learner is trained using the correlated features and accordingly Disease reports are classified. The target output identifies the category of a medical brain disease document based on a hierarchical classification scheme of the International Patent Classification (IPC) standard. Our approach is new to the medical domain and shows some improvement in the classification accuracy when compared to the other state of art classifier.
"SEMANTIC BASED TEXT CLASSIFICATION OF EMERGENT BRAIN TUMOUR DISEASE REPORTS USING MACHINE LEARNING TECHNIQUES", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.4, Issue 3, page no.156 - 161, March-2019, Available :https://ijsdr.org/papers/IJSDR1903028.pdf
Volume 4
Issue 3,
March-2019
Pages : 156 - 161
Paper Reg. ID: IJSDR_190187
Published Paper Id: IJSDR1903028
Downloads: 000347112
Research Area: Engineering
Country: -, -, -
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
