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Mobile Based Healthcare Tool an Integrated Disease Prediction & Recommendation System

Author

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  • Megha Rathi

    (Jaypee Institute of Information Technology, Noida, India)

  • Vikas Pareek

    (Mahatma Gandhi Central University, Motihari, India)

Abstract

Recent advances in mobile technology and machine learning together steer us to create a mobile-based healthcare app for recommending disease. In this study, the authors develop an android-based healthcare app which will detect all kinds of diseases in no time. The authors developed a novel, hybrid machine-learning algorithm in order to provide more accurate results. For the same purpose, the authors have combined two machine-learning algorithms, SVM and GA. The proposed algorithms will enhance the accuracy and at the same time reduce the complexity and count of attributes in the database. Analysis of algorithm is also done using statistical parameters like accuracy, confusion matrix, and roc-curve. The pivotal intent of this research work is to create an android-based healthcare app which will predict disease when provided with certain details. For a disease like cancer, for which a series of tests are required for confirmation, this app will quickly detect cancer and it is helpful to doctors as they can start the right course of treatment right away. Further, this app will also recommend a diet fitting the patient profile.

Suggested Citation

  • Megha Rathi & Vikas Pareek, 2019. "Mobile Based Healthcare Tool an Integrated Disease Prediction & Recommendation System," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 10(1), pages 38-62, January.
    • Handle: RePEc:igg:jkss00:v:10:y:2019:i:1:p:38-62
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJKSS.2019010103
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    Cited by:

    1. Piyanuch Arunrukthavon & Dittapong Songsaeng & Chadaporn Keatmanee & Songphon Klabwong & Mongkol Ekpanyapong & Matthew N. Dailey, 2022. "Diagnostic Performance of Artificial Intelligence for Interpreting Thyroid Cancer in Ultrasound images," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 13(1), pages 1-13, January.

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