Supervised Learning Technique for Prediction of Diseases

  • Bharti Yadav
  • Shilpi Sharma
  • Ashima Kalra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 624)


Lifestyle of a human being is changing day by day and leads human being to an unhealthy life. Apart from the routine exercise and healthy food, the health monitoring at a regular interval also becomes necessary to live long and healthy life. So, in this paper a system is proposed which will help in decreasing the progressive visits to the center moreover help in the early determination of risky sicknesses. This paper proposed a superior health monitoring framework utilizing neural network. In this, neural network (NN)-based health monitoring system is proposed as a solution to human health monitoring. In this, we are taking 276 instances and monitor their health by using 11 number of attributes. The dataset is got from UCI machine learning and from local specialist. All these attributes w.r.t patient are processed using NN in MATLAB and accordingly a result will be drafted. The previous techniques which were used to predict the heart, skin, liver disorder, diabetes, and cancer diseases are fuzzy logic, data mining, radial basis function network, recurrent network, etc. This paper presents our underlying attempt to grow such a framework with the assistance of NN by supervised learning method. In this proposed work, training part is 90% that means 248 instances used for training and rest 10% means 28 instances used for testing and validation. This system gives the more accurate result as compared to previous work, and it is the modified version of health monitoring system. This system shows an accuracy of 98.34% for training part which is the very good value for any data.


Artificial neural network Health Multilayer perceptron Attributes 



I Bharti Yadav hereby declare that half of the data used to do the testing of my above work is collected from a hospital (ethical committee) of live patient and another half is from a central repository on Internet. If any issue arises hereafter, then I will solely be responsible for this.


  1. 1.
    Konstantin koura, Themis P. Exarchos, Konstantina P. Exarchos, Michalis V. Karamouzis and Dimitrios I Fotiadis, “Machine learning applications in cancer prognosis and prediction”, computational and structural Biotechnology journal published Elsevier, vol 13, (2015), PP 8–17.Google Scholar
  2. 2.
    Giduthuri Sateesh Balri & Sundaram Suresh, “Sequential Projection Based Met cognitive Learning in a Radial Basis function Network for Classification Problems” IEEE transaction on neural networks and learning systems, vol 24 No 2 (2013).Google Scholar
  3. 3.
    Syed Umar Amin, Kavita Aggarwal and Dr. Rizwan Beg, “Genetic Neural Network Based Data Mining in Prediction of Heart Diseases using Risk factors”, Proceeding of 2013 IEEE conference on Information and Communication Technologies (ICT 2013).Google Scholar
  4. 4.
    Anbeeta R. Patel & Maulin M. Joshi “Heart Diseases Diagnosis using Neural Network”, 4th ICCCNT (IEEE), Tiruchengode, July 4–6 (2013).Google Scholar
  5. 5.
    Joyshril S. Sonawane & D.R Patil, “Prediction of Heart Diseases using multiplayer Perception Neural Network”, ICICES 2014-SA Engineering College, Chennai, Tamil nadu (IEEE 2014).Google Scholar
  6. 6.
    Jasdeep Singh Bhalla and Anmol Agarwal, “A Novel method for Medical disease diagnosis using artificial neural network”, The international conference on biomedical engineering and informatics 2008.Google Scholar
  7. 7.
    Gokul S, Shivachitra M and Vijayachitra S, “Parkinson’s disease prediction using machine learning approaches”, Proceeding IEEE 5th International Conference on advanced Computing 2013.Google Scholar
  8. 8.
    Ayush Anand and Divya Shakti, “Prediction of diabetes based on personal lifestyle indicators”, First International Conference on next generation computing technologies (IEEE) 2015.Google Scholar
  9. 9.
    Shikha Agarwal and Jitendra Agarwal, “Neural Network Technique for Cancer prediction; A Survey”, 19th International conference on knowledge based and intelligent information and engineering system, Published by Elsewier 2015.Google Scholar
  10. 10.
    T. Pamduranga Vital, G.S.V Prasada Raju, K. Sreemamurthy and V.P Venkata Charan, “A Probabilistic Neural Network Approach for classification of dataset collected from North Coastal Districts of AP, India using Matlab”, International Conference on intelligent computing, communication and convergence, Elsevier, procedia computer science 48 (2015).Google Scholar
  11. 11.
    I.A Basheer and M. Hajmeer, “Artificial neural network : fundamentals computing diseases and application”, Journal of micro biological methods 43, published by Elsevier 2000.Google Scholar
  12. 12.
    Dimitrios H. Mantzaris, George C. Anastassopoulos and Dimitrious K. Lymperopoulus, “Medical disease prediction using neural network”, Published by research gate 2008.Google Scholar
  13. 13.
    Dr K. Usha Rani, “Analysis of Heart Disease dataset using Neural Network”, International journal of data mining and knowledge management process, Volume 1, number 5, September 2011.Google Scholar
  14. 14.
    K. Balachandran and Dr R. Anitha, “Supervised Learning processing techniques for predignosis of lung Cancer diseases”, International journal of computer application (0975-8887) Volume 1 number 4 2011.Google Scholar
  15. 15.
    Muhammad Akmal Sapon, Khadijah Ismail Suehazlyn Zainudin, “Prediction of Diabetes by using artificial neural network”, International conference on circuits—systems and simulation Volume 7, 2011.Google Scholar
  16. 16.
    Dr R.R. Janghel, Dr Anupam Shukla and kshitij Verma, “Soft computing based expert system for hepatitis and Liver disorder”, 2nd IEEE International conference on engineering and technology (ICE Tech) 2016.Google Scholar
  17. 17.
    Xin Yao Senior Member, IEEE and Yong Liu, “A new evolutionary system for evolving Artificial Neural Networks”, IEEE Transaction on Neural Network Vol. 8, No-1, May 1997 Transactions on information Technology in Biomedicine Vol 11, No. 3, (2012)Google Scholar
  18. 18.
    E. Sathish, M. Sivachitra, Ramasamy Savitha, and S. Vijayachitra, “Wind profile prediction using a metacognitive fully complex-valued neural network,” IEEE Internation conference on Advanced computing (ICoAC), pp 1–6, (2012).Google Scholar
  19. 19.
    Kaur K. “Optical Multistage Interconnection Networks Using Neural Network Approach”, Master of Engineering, Thapar University (2009).Google Scholar
  20. 20.
    “Artificial Neural Networks in Medicine World Map” published in 2011, It was published in USENET, in July 21, (2011).Google Scholar
  21. 21.
    Mohammad A.M Abushariah, Assal A.M. Alquadah, Omar Y. Adwan, “Automatic Heart Disease diagnosis system based on Artificial neural network and adaptive neuro fuzzy inference system approaches”, Journal of software engineering and applications, 2014.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Chandigarh Group of CollegesLandranIndia

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