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Advanced Medical Diagnosis and Prediction Using Deep Learning


Affiliations
1 Department of CSE, UKF College of Engineering & Technology, Kerala, India
     

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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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  • Advanced Medical Diagnosis and Prediction Using Deep Learning

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Authors

Hasna Hashim
Department of CSE, UKF College of Engineering & Technology, Kerala, India
Nayama Grace Mathew
Department of CSE, UKF College of Engineering & Technology, Kerala, India
K. Sabira
Department of CSE, UKF College of Engineering & Technology, Kerala, India
A. Nizamudeen
Department of CSE, UKF College of Engineering & Technology, Kerala, India
Jithin Jacob
Department of CSE, UKF College of Engineering & Technology, Kerala, India

Abstract


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.

References