SLGP Header

Data Analysis Classifier Model for Prediction of Human Ailments Using Artificial Neural Network

IJCSEC Front Page

Recent trends has shown that health care delivered in industrialized nations often falls short of optimal, evidence based care. Clinical data are an ever growing source of information that is generated from the hospitals in the form of patient records, when these data are mined properly, the information hidden in these data are huge resource bank for medical research. These data often contain hidden patterns and relationships, which can lead to better diagnosis, better medicines, better treatment, and overall, a platform to better understand the mechanisms governing almost all aspects of the medical domain. Classification is a machine learning technique used to solve various problems like pattern classification, image processing, etc. Neural network is the effective tool for pattern classification. Classification of any data is important to know that the data are belongs to which group. With tremendous growing population, the doctors and experts available are not in proportion with the population. Hence it focuses on computing the probability of occurrence of a particular ailment from the medical data.
Given the rapidly growing population, the increased burden of chronic diseases and the increasing health care costs, there is an urgent need for the development, implementation, and deployment, in every day medical practice, of new models of health care services. In this scenario, home monitoring and data mining play an important role. Data mining is the computer assisted process of digging through and analyzing a large quantity of data in order to extract meaningful knowledge and to identify phenomena faster and better than human experts. Data Mining, also popularly known as Knowledge. Discovery in Databases (KDD), refers to the nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases. Data mining is widely used in business (insurance, banking, retail), science research (astronomy, medicine), and government security (detection of criminals and terrorists). Other similar terms referring to data mining are data dredging, knowledge extraction and pattern discovery to address the deficiencies in care, health care organizations are increasingly turning to clinical decision support systems, which provide clinicians with patient specific assessments or recommendations to aid clinical decision making. In recent trends, the statistics reveals that technology always stayed back when it came to diagnosis, a process that still requires a doctor’s knowledge and their experience to process the sheer number of variables involved, ranging from medical history to climatic conditions, temperature, environment and various other factors. Since the number of variables are greater than the total number of variables which no model has successfully analyzed yet. To overcome this problem, medical decision support system are becoming more and more important, which will assist doctors as well as medical students in taking correct decisions. Classification is a data mining technique used to group item based on some key characteristic. It is used to classify each item in a set of data into one of predefined set of classes or groups. It can deals with the large amount of data that are involved in processing. They are being used in different industry to easily identify the type and group to which a particular tuple belongs. Classification approaches normally use a training set where all objects are already associated with known class labels. The classification algorithm learns from the training set and builds a model. The model is used to classify new objects. There are many algorithms which are used for classification in data mining are following: 1. Decision tree induction 2. Nearest neighbor classifier 3. Artificial Neural Network


  1. [1] Han, J., Kamber, M. Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann publishers.
  2. Gupta, M., Aggarwal,N. Classification techniques analysis,NCCI2010- National Conference on Computational Instrumentation, CSIO Chandigarh, India, 19-20,March 2010.
  3. Kapadia, M.T., Lakhani, A. The integration of the back propagation algorithm into an autonomous robot control system, Dwarkadas j.Sanghvi College of engineering, vile parle, Mumbai.
  4. Makeshwar, M.S., Rajgure, N.K. Supervised ANN for a Classification Problems, International Journal of Computer Science and Application Issue 2010, ISSN 0974-0767.
  5. Naik, A.R., Pathan. S.K. Weather Classification and Forecasting using Back Propagation Feedforward Neural Network , International Journal of Scientific and Research Publications, Vol 2, Issue 12, December 2012.
  6. Rehman. M. Z., Nawi. N. M. Improving the Accuracy of Gradient Descent Back Propagation Algorithm (GDAM) on Classification Problems, International Journal on New Computer Architectures and Their Applications (IJNCAA) 1(4): 838-847, The Society of Digital Information and Wireless Communications, 2011 (ISSN: 2220-9085).
  7. Dhande.J. D., Dr.Gulhane.S.M. Design of Classifier Using Artificial Neural Network for Patients Survival Analysis, International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 1, Issue 2, November 2012
  8. Swain, M., Dash, S,K.., Dash,S and Mohapatra,A. An approach for iris plant classification using neural network, International Journal on Soft Computing ( IJSC ) Vol.3, No.1, February 2012
  9. Paulin.F. Santhakumaran.A. Classification of Breast cancer by comparing Back propagation training algorithms, International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 1 Jan 2011
  10. Dr. Rani, U.K. Analysis of heart diseases dataset using Neural Network Approach, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.1, No.5, September 2011
  11. Tuisima,S., Vachirapan,K and Sinthupinyo,S. Classification of Computer Game Addiction Level in Students in Secondary Education (M.1-3) using Neural Networks, 2012, 2nd International Conference on Management and Artificial Intelligence IPEDR Vol.35 (2012) © (2012) IACSIT Press, Singapore
  12. Gupta,A., Shreevastava, M. Medical Diagnosis using Back propagation Algorithm, International Journal of Emerging Technology and Advanced Engineering, Volume 1, Issue 1,November 2011