A new approach for prediction of lung carcinoma using back propogation neural network with decision tree classifiers

  • R. Varadharajan
  • M. K. Priyan
  • Parthasarathy PanchatcharamEmail author
  • S. Vivekanandan
  • M. Gunasekaran
Original Research


Carcinoma otherwise called Cancer is the normally developing and most risky ailment happened in human species. Lung Carcinoma is one of them. It is an ailment that happens because of uncontrolled development of harmful cells in the tissues of the lungs. Earlier determination of the ailment spares immense number of lives, bombing in which may prompt other extreme issues causing sudden deadly demise. Thought process of this framework is to mechanize the recognition procedure in order to perform propelled identification of the malady in its beginning period. In this paper an investigation was made to examine the lung tumour expectation utilizing classification algorithms, for example, back propogation neural network and decision tree. At first 20 tumour and non-disease patients’ examples information were gathered with 30 qualities, pre-prepared and dissected utilizing classification algorithms and later a similar methodology was actualized on 50 occurrences (50 Cancer patients and 10 non growth patients). The informational indexes utilized as a part of this examination are taken from UCI data sets for patients affected by lung cancer and Michigan Lung Cancer patient’s informational index. The principle point of this paper is to give the prior notice to the clients and to quantify the execution investigation of the classification algorithms utilizing WEKA Tool. Test comes about demonstrate that the previously mentioned calculation has promising outcomes for this reason with the general forecast exactness of 94 and 95.4%, separately. Another way to deal with identifies the lungs tumour by Decision tree and BPNN calculation will give viable outcome as contrast with other calculation. The proposed framework will improve the execution of prediction and classification.


Back propogation Decision tree Lung cancer Classification algorithms 



  1. Adams WP, Johnson TA (1994) Improved linear programming-based lower bounds for the quadratic assignment problem. DIMACS Ser Discret Math Theor Comput Sci 16:43–75MathSciNetCrossRefGoogle Scholar
  2. Ahyaningsih F (2017) A combined strategy for solving quadratic assignment problem. Proc AIP Conf 1867(1):020006CrossRefGoogle Scholar
  3. Alatas B (2011) ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst Appl 38(10):13170–13180CrossRefGoogle Scholar
  4. Ben-David G, Malah D (2005) Bounds on the performance of vector-quantizers under channel errors. IEEE Trans Inf Theory 51(6):2227–2235MathSciNetCrossRefGoogle Scholar
  5. Benjaafar S (2002) Modeling and analysis of congestion in the design of facility layouts. Manag Sci 48(5):679–704CrossRefGoogle Scholar
  6. Benlic U, Hao JK (2013) Breakout local search for the quadratic assignment problem. Appl Math Comput 219(9):4800–4815MathSciNetzbMATHGoogle Scholar
  7. Chandra I, Sivakumar N, Gokulnath CB, Parthasarathy P (2018) IoT based fall detection and ambient assisted system for the elderly. Cluster Comput. CrossRefGoogle Scholar
  8. D´ıaz-Uriarte R, de Andréś A (2006) Gene selection and classification of microarray data using random forest. BMC Bioinform 7(1):3CrossRefGoogle Scholar
  9. Hornik K, Stinchcombe M, White H (1990) Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Netw 3:551–560. CrossRefGoogle Scholar
  10. Kanisha B, Lokesh S, Kumar PM, Parthasarathy P, Babu C, G (2018) Speech recognition with improved support vector machine using dual classifiers and cross fitness validation. Pers Ubiquitous Comput 1–9Google Scholar
  11. Karaolis MA, Moutiris JA, Hadjipanayi D, Pattichis CS (2010) Assessment of the risk factors of coronary heart events based on data mining with decision trees. IEEE Trans Inf Technol Biomed 14:559–566. CrossRefGoogle Scholar
  12. Krishnaiah V, Narsimha G, Subhash Chandra N (2013) Diagnosis of lung cancer prediction system using data mining classification techniques. Int J Comput Sci Inf Technol 4(1):39–45Google Scholar
  13. Kumar PM, Lokesh S, Varatharajan R, Babu GC, Parthasarathy P (2018) Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Future Gener Comput Syst 86:527–534CrossRefGoogle Scholar
  14. Lokesh S, Kumar PM, Devi MR, Parthasarathy P, Gokulnath C (2018) An automatic tamil speech recognition system by using bidirectional recurrent neural network with self-organizing map. Neural Comput Appl. CrossRefGoogle Scholar
  15. Mathan K, Kumar PM, Panchatcharam P, Manogaran G, Varadharajan R (2018) A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Design Autom Embed Syst 22(3):225–242CrossRefGoogle Scholar
  16. Padmavathy TV, Vimalkumar MN, Nagarajan S, Babu GC, Parthasarathy P (2018) Performance analysis of pre-cancerous mammographic image enhancement feature using non-subsampled shearlet transform. Multimed Tools Appl. CrossRefGoogle Scholar
  17. Park SM, Lim MK, Shin SA, Yun YH (2006) Impact of prediagnosis smoking, alcohol, obesity and insulin resistance on survival in Male cancer Patients: National Health Insurance corporation study. J Clin Oncol 24(31):132–140CrossRefGoogle Scholar
  18. Parthasarathy P, Vivekanandan S (2018a) A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases. Inform Med Unlocked 12:143–147CrossRefGoogle Scholar
  19. Parthasarathy P, Vivekanandan S (2018b) A typical IoT architecture-based regular monitoring of arthritis disease using time wrapping algorithm. Int J Comput Appl. CrossRefGoogle Scholar
  20. Parthasarathy P, Vivekanandan S (2018c) Investigation on uric acid biosensor model for enzyme layer thickness for the application of arthritis disease diagnosis. Health Inf Sci Syst 6:1–6CrossRefGoogle Scholar
  21. Qiang Y, Guo Y, Li X, Wang Q, Chen H, Cuic D (2007) The diagnostic rules of peripheral lung cancer preliminary study based on data mining technique. J Nanjing Med Univ 21(3):190–195CrossRefGoogle Scholar
  22. Qiang Y, Guo Y, Li X, Wang Q, Chen H, Cuic D (2014) The diagnostic rules of peripheral lung cancer preliminary study based on data mining technique. J Nanjing Med Univ 21(3):190–195CrossRefGoogle Scholar
  23. Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106Google Scholar
  24. Rahman RM, Md. Hasan FR (2011) Using and comparing different decision tree classification techniques for mining ICDDR, B Hospital Surveillance data. Expert Syst Appl 38:11421–11436. CrossRefGoogle Scholar
  25. Rao BS, Rao KN, Setty SP (2014) An approach for heart disease detection by enhancing training phase of neural network using hybrid algorithm. In: 2014 IEEE International conference on advance in computer, pp 1211–1220Google Scholar
  26. Saini SK, Gaurav AC (2014) Detection of lung carcinoma using fuzzy logic and ACO techniques. IJERT 3(8):903–906Google Scholar
  27. Subbulakshmi CV, Deepa SN (2015) Medical dataset classification: a machine learning paradigm integrating particle swarm optimization with extreme learning machine classifier. Sci World J 2015:1–12. CrossRefGoogle Scholar
  28. Tu MC, Shin D, Shin D (2009) Effective diagnosis of heart disease through bagging approach. In: 2nd international conference on Biomedical engineering and informatics, 2009, BMEI’09, pp 1–4Google Scholar
  29. Weng C-H, Huang TC-K, Han R-P (2016) Disease prediction with different types of neural network classifiers. Telemat Inform 33:277–292. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • R. Varadharajan
    • 1
  • M. K. Priyan
    • 2
  • Parthasarathy Panchatcharam
    • 2
    Email author
  • S. Vivekanandan
    • 2
  • M. Gunasekaran
    • 3
  1. 1.Sri Ramanujar Engineering CollegeChennaiIndia
  2. 2.VIT UniversityVelloreIndia
  3. 3.University of CaliforniaDavisUSA

Personalised recommendations