A Computational System for Disease Diagnosis and Prescription Generation
Nishant Bakhrey1, Riya Bakhtiani2, Jainam Soni3, Dhananjay Kalbande4
1Nishant Bakhrey*, B.E, Department of Computer Engineering, Sardar Patel Institute of Technology, Munshi Nagar, Andheri (West), Mumbai
2Riya Bakhtiani, B.E, Department of Computer Engineering, Sardar Patel Institute of Technology, Munshi Nagar, Andheri (West), Mumbai
3Jainam Soni, B.E, Department of Computer Engineering, Sardar Patel Institute of Technology, Munshi Nagar, Andheri (West), Mumbai
4Dr.Dhananjay Kalbande, Professor, Department of Computer Engineering, Sardar Patel Institute of Technology, Munshi Nagar, Andheri (West), Mumbai
Manuscript received on November 12, 2019. | Revised Manuscript received on 21 November, 2019. | Manuscript published on December 10, 2019. | PP: 1906-1910 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6262129219/2019©BEIESP | DOI: 10.35940/ijitee.B6262.129219
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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: Illnesses should be dealt with proper care and understanding and on time. If failed to be treated on time, they pave the way for numerous medical issues and these issues can lead to fatal deterioration of health, and in worst case death. These issues are winding up more awful in the villages because of the shortage of authorities, specialists and healthcare centres. To mention as a fact, the practitioners in Primary Health Care Centres (PHC) who provide 80% of outpatient care, don’t have proper qualifications for it. Considering the scenario of rural India, more than 8% of 25,300 primary health centres in the country were operating without a proper doctor, 38% had absence of a laboratory technician, and 22% had no pharmacist. The reason for the shortages of educated doctors is that trained and city-bred doctors are unwilling to serve in rural areas. In this paper, with a goal to address such issues, we have made endeavors to plan and create a master framework for practitioners and patients to enter the symptoms and find out the relevant disease. For most users, however, just the recognition of the disease is not helpful. This paper introduces a novel concept to augment the result of the recognized disease with affinity analysis to perform market basket analysis to help users identify their symptoms more easily and ameliorate the set of symptomsinput for classification. Later, it takes into consideration any particular allergy of the user while generating prescription. This system possess enormous advantage in diagnosing diseases especially in rural areas and provide adequate and appropriate results and also makes reliable predictions to users. For achieving this, we use Decision Tree Classifier on the symptom disease dataset and a content based recommendation system for generating the appropriate prescription. Index
Keywords: Disease Prediction, Prescription, Decision Tree Classifier, Market Basket Analysis
Scope of the Article: Computational Biology