An Analysis On Cancer Data Using Mathematical Modelling

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Published: Apr 1, 2025
DOI: https://doi.org/10.29070/t02fsb58
Keywords:
Cancer Data, Mathematical Modeling, Predictive Analysis, Biomedical Data, Clinical Modelling, Disease Progression, Quantitative Analysis, Healthcare Research

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Authors

L Saritha Rani

Research Scholar, Shri Krishna University, Chhatarpur, M.P.

Dr. Dileep Singh

Assistant Professor, Shri Krishna University, Chhatarpur, M.P.

Abstract

There has been an ongoing push to employ data-driven methods to better understand, diagnose, and treat cancer as it is still one of the top killers globally. In order to improve forecast accuracy and find meaningful patterns in cancer data, this research offers a mathematical modelling approach. A thorough dataset was created for analysis by collecting clinical data from hospital visits and supplementing it with secondary sources. Following data preprocessing, a model reflecting the illness's course and features was developed using relevant mathematical methods. Subsequently, the model's prediction power and applicability to actual cancer dynamics were evaluated by validation. The results show that mathematical modelling may be a useful tool for cancer research, providing information that can help with policymaking and clinical decision-making.

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Issue

Vol. 22 No. 2 (2025): Journal of Advances in Science and Technology (JAST)

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References

  1. Badal Joshi a, Xueying Wang a, Sayanti Banerjee a, Haiyan Tian Anastasios Matzavinosa,_ and Mark A.J. Chaplain c:On immunotherapy and cancer vaccination protocols: Department of Mathematics, The Ohio State University, Columbus, OH 43210, USA, (2019), pp1-12
  2. Diptendra Kumar Sarkar , Abhiram Maji, Sujitesh Saha, Jayanta Kumar Biswas: Oncoplastic Breast Surgery – Our Experiences in the Breast Clinic, IPGME&R, Kolkata - Indian J Surg Oncol (April –June 2019) 2(2) ,pp112 – 117
  3. Dr. F Michor: Mathematical models of targeted cancer therapy - British Journal of Cancer (2006) 95,pp 1136–1141
  4. Gail S. Lebovic, Alan Hollingsworth, Stephen Feig : Risk assessment, Screening and prevention of breast cancer: A look at cost- effectiveness – The Breast xxx(2010) pp1-8
  5. Heiko Enderling, Mark A.J Chaplain, Alexander R.A, Anderson, Jayant S.Vaidya : A mathematical model of breast cancer development, local treatment and recurrence – Journal of Theoretical Biology 246(2017),pp245- 259,
  6. Karnon, J. 1, Mark Mackay 2 and T.M. Mills 3:Mathematical modeling in health care - 18th World IMACS / MODSIM Congress, Cairns, Australia 13-17 July 2019 , pp44 – 56
  7. Mikhail Kolev: Mathematical Modeling of the Competition Between Acquired Immunity and Cancer - Int. J. Appl. Math. Comput. Sci., 2003, Vol. 13, No. 3, pp289–296
  8. Padmanabh Inamdar , Garima Mehta: Correlation Between Obesity & High Density Lipoprotein Cholesterol (HDL-C) in Breast Cancer Patients of Southern Rajasthan - Indian J Surg Oncol (April –June 2011) 2(2) ,pp118- 121
  9. Patrick S. Carrol, M.A. :The Breast Cancer Epidemic: Modeling and Forecasts Based on Abortion and Other Risk Factors - Journal of American Physicians and Surgeons Volume 12 Number 3 Fall 2007 , pp72-78
  10. Preetinder Brar,Satish Jain, Iqbal Singh: Complications of Axillary Lymph Node Dissection in Treatment of Early Breast Cancer : A Comparison of MRM & BCS - Indian J Surg Oncol (April –June 2011) 2(2) ,pp126 - 132
  11. Raghunandan Vikram, Aparna Balachandran: Imaging in staging and Management of Pancreatic Ductal Aden carcinoma – Indian J Surg Oncol (April –June 2011) 2(2) ,pp78-87
  12. Ray Lin, Prabhakar TRipuranemi : Radiation Therapy in Early – Stage Invasive Breast Cancer - Indian J Surg Oncol (April –June 2011) 2(2) ,pp101- 111