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Early detection of regional symptomatic diseases can lead to a decreased impact on populations. Without having clear information about necessary conditions for earlier detection and their influencing factors, attempts to improve surveillance will be unsystematic. Systematic methods can be developed by considering large symptomatic data and natural language processing. Those symptomatic data is available in special comments made by paramedic/doctor during physiological data acquisition from A3 Critiview. We can access those special comments and location of it from SQL database and A3 cloud server respectively. Natural language processing on statistical data analysis can find out common diseases over particular locations. The software used for designing the proposed system is Python 3


regional symptomatic disease; natural language processing; physiological data acquisition; statistical data analysis

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How to Cite
Shweta Suresh Mahavarkar, Dr. A. N. Cheeran, and Shweta Yadav, “DETECTION OF DISEASES OVER GEOGRAPHICALLY DIVERSE LOCATIONS USING MEDICAL SYMPTOMATIC DATA AND NATURAL LANGUAGE PROCESSING”, IEJRD - International Multidisciplinary Journal, vol. 5, no. 5, p. 7, Jun. 2020.


  1. M. Kim, J. Y. Lee, and H. Kim, “Warning and detection system for epidemic disease,” 2016 International Conference on Information and Communication Technology Convergence (ICTC), 2016.
  2. K. K. Singh, “An Artificial Intelligence and Cloud Based Collaborative Platform for Plant Disease Identification, Tracking and Forecasting for Farmers,” 2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), 2018.
  3. P. S. Kohli and S. Arora, “Application of Machine Learning in Disease Prediction,” 2018 4th International Conference on Computing Communication and Automation (ICCCA), 2018.
  4. F. Aofa, P. S. Sasongko, Sutikno, Suhartono, and W. A. Adzani, “Early Detection System Of Diabetes Mellitus Disease Using Artificial Neural Network Backpropagation With Adaptive Learning Rate And Particle Swarm Optimization,” 2018 2nd International Conference on Informatics and Computational Sciences (ICICoS), 2018.
  5. B. Norman, T. Davis, S. Quinn, R. Massey, and D. Hirsh, “Automated identification of pediatric appendicitis score in emergency department notes using natural language processing,” 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2017.
  6. F. B. Putra, A. A. Yusuf, H. Yulianus, Y. P. Pratama, D. S. Humairra, U. Erifani, D. K. Basuki, S. Sukaridhoto, and R. P. N. Budiarti, “Identification of Symptoms Based on Natural Language Processing (NLP) for Disease Diagnosis Based on International Classification of Diseases and Related Health Problems (ICD-11),” 2019 International Electronics Symposium (IES), 2019..
  7. K. Duangchaemkarn, V. Chaovatut, P. Wiwatanadate, and E. Boonchieng, “Symptom-based data preprocessing for the detection of disease outbreak,” 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017.
  8. D. Feng, Y. Baozong, and L. Biqin, “Extracting entities for natural language dialog system,” WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.
  9. "Disease outbreaks by year", World Health Organization, 2020. [Online]. Available: [Accessed: 18- Mar-2020]