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Pancreatic Cancer Detection Using Radial Basis Neural Network

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Abstract

Pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, is a leading cause of cancer death in both men and women. Early detection of PDAC is challenging and often patients are diagnosed when the cancer is well developed. Recent research has tried to establish a relationship between protein biomarker levels in urine samples and pancreatic cancer to allow non-invasive, inexpensive and possible early detection of cancer. Artificial neural networks, used to solve highly complex problems within the physical sciences, can efficiently facilitate cancer detection. This paper presents a neural network model to diagnose pancreatic cancer using four urinary biomarkers- creatinine, LYVE1, REGB1, TFF1. The model used is Radial basis function neural network (RBFNN) and is created using MATLAB software to differentiate between urine sample data of healthy people and cancer patients. It produces an accuracy of 76.5%, recall of 77% and specificity of 76% suggesting possible diagnosis of PDAC patients.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the figshare repository  https://doi.org/10.6084/m9.figshare.22138340.v2. These datasets were derived from the following public domain resources: https://www.kaggle.com/datasets/johnjdavisiv/urinary-biomarkers-for-pancreatic-cancerhttps://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003489#abstract0

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This research received no external funding.

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Authors and Affiliations

  1. Department of IT, Thakur College of Science and Commerce, Thakur Village, Kandivali (East), Mumbai, 400101, Maharashtra, India

    Anand Upadhyay, Amisha Lalwaney & Ajay Sharma

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  1. Anand Upadhyay
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  2. Amisha Lalwaney
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  3. Ajay Sharma
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Correspondence to Anand Upadhyay.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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This article is part of the topical collection “Advances in Computational Intelligence for Artificial Intelligence, Machine Learning, Internet of Things and Data Analytics” guest edited by S. Meenakshi Sundaram, Young Lee and Gururaj K S.

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Upadhyay, A., Lalwaney, A. & Sharma, A. Pancreatic Cancer Detection Using Radial Basis Neural Network. SN COMPUT. SCI. 4, 261 (2023). https://doi.org/10.1007/s42979-022-01643-7

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