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Performance of Statistical and Neural Network Method for Prediction of Survival of Oral Cancer Patients

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Advances in Computing Applications

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Abstract

Traditionally, standard statistical methods by direct hands-on data analysis were used for prediction, as the data were simple and volume was less. However, the increasing volume of data and its complex nature has motivated the study of automatic data analysis using artificial neural networks with the help of more sophisticated tools which can operate directly on data. This paper presents a case study to predict the survival rate of oral malignancy patients, with the help of two predictive models, linear regression (LR), which is a contemporary statistical model, and multilayer perceptron (MLP), which is an artificial neural network model. The data of more than 1000 patients who visited tertiary care center during June 2004 to June 2009 are collected through active case finding method and are used to build models. Analytical comparison of the performance of both the models is carried out. The experimental result shows that the classification accuracy of MLP model is 70.05 % and of LR model is 60.10 %. After comparing on various evaluation criteria, it is concluded that the MLP model is certainly a better model to predict the survival rate of oral malignancy patients, in comparison with LR model. Besides, there are many other benefits of neural networks, such as less formal statistical training needed, ability to detect nonlinear complex relationships between independent and dependent variables, capability to diagnose most practicable interactions concerning predictor factors, as well as the ease of use of several training algorithms.

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Acknowledgements

The authors would like to place on record their sincere thank Dr. Vijay Sharma, MS, ENT, for his valuable contribution in understanding the occurrence and diagnosis of Oral Cancer. The authors offer their gratitude to the management and staff of Indian School of Mines, for their constant support.

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Authors and Affiliations

  1. Zeal Institute of Business Administration, Computer Application and Research, Savitribai Phule Pune University, Ganesh Khind, 411007, Pune, Maharashtra, India

    Neha Sharma

  2. Computer Science and Engineering Department, Indian School of Mines, Dhanbad, Jharkhand, India

    Hari Om

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  1. Neha Sharma
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  2. Hari Om
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Correspondence to Neha Sharma .

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Editors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta School of Information Technology, Kolkata, West Bengal, India

    Amlan Chakrabarti

  2. Zeal Institute of Business Administration, Computer Application and Research, Zeal Education Society's, Pune, Maharashtra, India

    Neha Sharma

  3. Faculty of Engineering, Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Arad, Romania

    Valentina Emilia Balas

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Sharma, N., Om, H. (2016). Performance of Statistical and Neural Network Method for Prediction of Survival of Oral Cancer Patients. In: Chakrabarti, A., Sharma, N., Balas, V. (eds) Advances in Computing Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-2630-0_16

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  • DOI: https://doi.org/10.1007/978-981-10-2630-0_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2629-4

  • Online ISBN: 978-981-10-2630-0

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