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Adaptive Artificial Bee Colony (AABC)-Based Malignancy Pre-Diagnosis

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Data Science and Security

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 132))

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Abstract

Lung cancer is one of the leading causes of death. The survival rate of the patients diagnosed with lung cancer depends on the stage of the detection and the timely prognosis. Hence, early detection of anomalous malignant cells is needed for pre-diagnosis of lung cancer as it plays a major role in the prognosis and treatment. In this work, an innovative pre-diagnosis approach is suggested, wherein the size of the dataset comprising risk factors and symptoms is considerably decreased and optimized by means of an Adaptive Artificial Bee Colony (AABC) algorithm. Subsequently, the optimized dataset is fed to the Feed-Forward Back-Propagation Neural Network (FFBNN) to perform the training task. For the testing, supplementary data is furnished to well-guided FFBNN-AABC to authenticate whether the supplied investigational data is competent to effectively forecast the lung disorder or not. The results obtained show a considerable improvement in the classification performance compared to other approaches like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

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Acknowledgements

Authors wishes to acknowledge the technical and infrastructural help rendered by the faculty members of Department of CSE of CHRIST (Deemed to be University), India.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Bangalore, 560074, India

    Sujatha Arun Kokatnoor & Balachandran Krishnan

Authors
  1. Sujatha Arun Kokatnoor
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  2. Balachandran Krishnan
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Corresponding author

Correspondence to Sujatha Arun Kokatnoor .

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Editors and Affiliations

  1. Department of Computer Science, Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  2. CHRIST (Deemed to be University), Pune Lavasa Campus, Lavasa, India

    Samiksha Shukla

  3. Stanford University, Stanford, CA, USA

    Aynur Unal

  4. Computer Science and Engineering, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

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Kokatnoor, S.A., Krishnan, B. (2021). Adaptive Artificial Bee Colony (AABC)-Based Malignancy Pre-Diagnosis. In: Jat, D.S., Shukla, S., Unal, A., Mishra, D.K. (eds) Data Science and Security. Lecture Notes in Networks and Systems, vol 132. Springer, Singapore. https://doi.org/10.1007/978-981-15-5309-7_15

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