Clinical Text Mining of Electronic Health Records to Classify Leprosy Patients Cases
Jalpa Mehta1, Jaydeep Dharamsey2, Pravalika Domal3, V. V. Pai4
1Ms. Jalpa Mehta*, Assistant Professor, Department of Information Technology, Shah and Anchor Kutchhi Engineering College, Mumbai, India.
2Mr. Jaydeep Dharamsey, Department of Information Technology, Shah and Anchor Kutchhi Engineering College, Mumbai, India.
3Ms. Pravalika Domal, Department of Information Technology, Shah and Anchor Kutchhi Engineering College, Mumbai, India.
4Dr. Vivek Vasudev Pai, Director, Department of Bombay Leprosy Project, Mumbai, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 24, 2020. | Manuscript published on March 10, 2020. | PP: 2331-2336 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2464039520/2020©BEIESP | DOI: 10.35940/ijitee.E2464.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Leprosy is one of the major public health problems and listed among the neglected tropical diseases in India. It is also called Hansen’s Diseases (HD), which is a long haul contamination by microorganisms Mycobacterium leprae or Mycobacterium lepromatosis. Untreated, leprosy can dynamic and changeless harm to the skin, nerves, appendages, and eyes. This paper intends to depict classification of leprosy cases from the main indication of side effects. Electronic Health Records (EHRs) of Leprosy Patients from verified sources have been generated. The clinical notes included in EHRs have been processed through Natural Language Processing Tools. In order to predict type of leprosy, Rule based classification method has been proposed in this paper. Further our approach is compared with various Machine Learning (ML) algorithms like Support Vector Machine (SVM), Logistic regression (LR) and performance parameters are compared.
Keywords: Clinical Text Mining, Natural Language Processing, Leprosy, Support Vector Machine, Logistic regression, Rule based, Electronic Record, Clinical Notes.
Scope of the Article: Natural Language Processing.