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Advanced Medical Diagnosis and Prediction Using Deep Learning
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A significant part of human life is medical diagnosis. The correct prediction of a disease revolves around various steps. Due to the tremendous advancement in the medical field, the data is quite large, which makes it challenging for medical diagnosis and prediction. Mobile health applications are becoming increasingly used as medical diagnosis. The use of Electronic Health Record (EHR) by hospitals is also increasing. These systems have many advantages such as easier access to patient data, structured information and support for decision making and better access to information. This paper focus on such a mobile application that enables the users to predict or diagnose medical conditions based on the symptoms provided by the user.
Keywords
Android, Decision Tree, Flask, Machine Learning, Medical Diagnosis.
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- A. Jutel, and D. Lupton, “Digitizing diagnosis: A review of mobile applications in the diagnostic process,” Diagnosis, vol. 2, no. 2, pp. 89-96, 2015.
- T. Bläsing, L. Batyuk, A. D. Schmidt, S. A. Camtepe, and S. Albayrak, “An Android Application Sandbox system for suspicious software detection,” IEEE 5th Int. Conf. on Malicious and Unwanted Software, France, 2010.
- R. A. Soni, “A study paper on Android UI,” International Journal of Enterprise Computing and Business Systems, vol. 2, no. 1, 2013.
- S. Devi, and A. Kalia, “Study of data cleaning & comparison of data cleaning tools,” International Journal of Computer Science and Mobile Computing, vol. 4, no. 3, pp. 360-370, 2015.
- R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural language processing (almost) from scratch,” Journal of Machine Learning Research, vol. 12, pp. 2493-2537, 2011.
- J. N. Mamman, M. B. Abdullahi, J. KoloAlhassan, and S. A. Adepoju, “On the use of decision tree for treatment options in medical decision,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, no. 2, pp. 71-78, 2015.
- C. M. Bishop, Pattern Recognition and Machine Learning, Springer, New York, 2006.
- I. Kononenko, “Machine learning for medical diagnosis: History, state of the art perspective,” Artificial Intelligence in Medicine, vol. 23, no. 1, pp. 89-109, 2001.
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