A Gaussian Process Framework for Prognostication and Visualization in Dermatological Oncology

Md Habibur Rahman; Nabilah Hossain Sarker; Md Musfique Anwar; Mufti Mahmud; David J. Brown; Muhammad Arifur Rahman

doi:10.1007/978-981-96-6960-8_24

A Gaussian Process Framework for Prognostication and Visualization in Dermatological Oncology

Md Habibur Rahman
, Nabilah Hossain Sarker
, Md Musfique Anwar
, Mufti Mahmud
, David J. Brown
, Muhammad Arifur Rahman^*

^*Corresponding author for this work

Department of Information and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Data-driven predictive models in epidemiological studies remain unsatisfactory for a range of reasons. One issue includes a lack of non-parametric data-driven predictive models underpinning research in this field. The Gaussian Process is a state-of-art non-parametric data driven Bayesian framework used widely for Machine Learning tasks from regression, classification to clustering. The main challenge is to design a valid kernel. Cancer is the second leading worldwide cause of death and skin cancer is one of the most common causes of cancer death. Our research uses an aggregated data set composed of skin cancer tumors diagnosed from 1995–2017 (ICD-10 C43x - C44x), collected within the UK. This data set includes counts grouped by age group, sex, and diagnosis year for all melanoma and non-melanoma skin cancers. In this work, the Gaussian Process framework is applied, alongside the introduction of a novel kernel and features including age limit, gender, and type of cancer which resulted in a prediction model able to predict the trend for the upcoming 8 years (up to 2025).

Original language	English
Title of host publication	Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings
Editors	Mufti Mahmud, Maryam Doborjeh, Zohreh Doborjeh, Kevin Wong, Andrew Chi Sing Leung, M. Tanveer
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	341-355
Number of pages	15
ISBN (Print)	9789819669592
DOIs	https://doi.org/10.1007/978-981-96-6960-8_24
State	Published - 2025
Event	31st International Conference on Neural Information Processing, ICONIP 2024 - Auckland, New Zealand Duration: 2 Dec 2024 → 6 Dec 2024

Publication series

Name	Communications in Computer and Information Science
Volume	2286 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	31st International Conference on Neural Information Processing, ICONIP 2024
Country/Territory	New Zealand
City	Auckland
Period	2/12/24 → 6/12/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Keywords

Epidemiology
Gaussian Process
Kernel
Skin Cancer

ASJC Scopus subject areas

General Computer Science
General Mathematics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-96-6960-8_24

Cite this

Rahman, M. H., Sarker, N. H., Anwar, M. M., Mahmud, M., Brown, D. J., & Rahman, M. A. (2025). A Gaussian Process Framework for Prognostication and Visualization in Dermatological Oncology. In M. Mahmud, M. Doborjeh, Z. Doborjeh, K. Wong, A. C. S. Leung, & M. Tanveer (Eds.), Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings (pp. 341-355). (Communications in Computer and Information Science; Vol. 2286 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-6960-8_24

Rahman, Md Habibur ; Sarker, Nabilah Hossain ; Anwar, Md Musfique et al. / A Gaussian Process Framework for Prognostication and Visualization in Dermatological Oncology. Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings. editor / Mufti Mahmud ; Maryam Doborjeh ; Zohreh Doborjeh ; Kevin Wong ; Andrew Chi Sing Leung ; M. Tanveer. Springer Science and Business Media Deutschland GmbH, 2025. pp. 341-355 (Communications in Computer and Information Science).

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Rahman, MH, Sarker, NH, Anwar, MM, Mahmud, M, Brown, DJ & Rahman, MA 2025, A Gaussian Process Framework for Prognostication and Visualization in Dermatological Oncology. in M Mahmud, M Doborjeh, Z Doborjeh, K Wong, ACS Leung & M Tanveer (eds), Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings. Communications in Computer and Information Science, vol. 2286 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 341-355, 31st International Conference on Neural Information Processing, ICONIP 2024, Auckland, New Zealand, 2/12/24. https://doi.org/10.1007/978-981-96-6960-8_24

A Gaussian Process Framework for Prognostication and Visualization in Dermatological Oncology. / Rahman, Md Habibur; Sarker, Nabilah Hossain; Anwar, Md Musfique et al.
Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings. ed. / Mufti Mahmud; Maryam Doborjeh; Zohreh Doborjeh; Kevin Wong; Andrew Chi Sing Leung; M. Tanveer. Springer Science and Business Media Deutschland GmbH, 2025. p. 341-355 (Communications in Computer and Information Science; Vol. 2286 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Anwar, Md Musfique

AU - Mahmud, Mufti

AU - Brown, David J.

AU - Rahman, Muhammad Arifur

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - Data-driven predictive models in epidemiological studies remain unsatisfactory for a range of reasons. One issue includes a lack of non-parametric data-driven predictive models underpinning research in this field. The Gaussian Process is a state-of-art non-parametric data driven Bayesian framework used widely for Machine Learning tasks from regression, classification to clustering. The main challenge is to design a valid kernel. Cancer is the second leading worldwide cause of death and skin cancer is one of the most common causes of cancer death. Our research uses an aggregated data set composed of skin cancer tumors diagnosed from 1995–2017 (ICD-10 C43x - C44x), collected within the UK. This data set includes counts grouped by age group, sex, and diagnosis year for all melanoma and non-melanoma skin cancers. In this work, the Gaussian Process framework is applied, alongside the introduction of a novel kernel and features including age limit, gender, and type of cancer which resulted in a prediction model able to predict the trend for the upcoming 8 years (up to 2025).

AB - Data-driven predictive models in epidemiological studies remain unsatisfactory for a range of reasons. One issue includes a lack of non-parametric data-driven predictive models underpinning research in this field. The Gaussian Process is a state-of-art non-parametric data driven Bayesian framework used widely for Machine Learning tasks from regression, classification to clustering. The main challenge is to design a valid kernel. Cancer is the second leading worldwide cause of death and skin cancer is one of the most common causes of cancer death. Our research uses an aggregated data set composed of skin cancer tumors diagnosed from 1995–2017 (ICD-10 C43x - C44x), collected within the UK. This data set includes counts grouped by age group, sex, and diagnosis year for all melanoma and non-melanoma skin cancers. In this work, the Gaussian Process framework is applied, alongside the introduction of a novel kernel and features including age limit, gender, and type of cancer which resulted in a prediction model able to predict the trend for the upcoming 8 years (up to 2025).

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Rahman MH, Sarker NH, Anwar MM, Mahmud M, Brown DJ, Rahman MA. A Gaussian Process Framework for Prognostication and Visualization in Dermatological Oncology. In Mahmud M, Doborjeh M, Doborjeh Z, Wong K, Leung ACS, Tanveer M, editors, Neural Information Processing - 31st International Conference, ICONIP 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2025. p. 341-355. (Communications in Computer and Information Science). doi: 10.1007/978-981-96-6960-8_24