Integrating ANFIS, ANN, & MLR with MCDM for Accurate Prediction of Lung Cancer for Improved Clinical Decision Support

Efe Precious Onakpojeruo; Berna Uzun; Leena R. David; Ilker Ozsahin; Christiana Chioma Efe; Dilber Uzun Ozsahin

doi:10.1109/DeSE63988.2024.10912008

Integrating ANFIS, ANN, & MLR with MCDM for Accurate Prediction of Lung Cancer for Improved Clinical Decision Support

Efe Precious Onakpojeruo
, Berna Uzun
, Leena R. David
, Ilker Ozsahin
, Christiana Chioma Efe
, Dilber Uzun Ozsahin

University of Sharjah

Near East University
Manipal Academy of Higher Education

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

Abstract

The lungs regulate breathing and supply oxygen to every cell in the body. Simultaneously, they act as air filters, blocking potentially harmful particles and microbes from entering the respiratory system. Humans have built-in defences that keep their lungs safe. However, these cannot guarantee complete protection against lung diseases. The lungs are vulnerable to infection, inflammation, and malignant tumors. The aim of this research is to investigate the applications of three models, specifically an ANN, ANFIS, and a classical linear regression MLR for the prediction of lung cancer. To evaluate the models, we have considered four performance parameters: R², MSE, RMSE, and R. To make the research robust, the study also deployed an MCDM tool called the fuzzy PROMETHEE to evaluate, compare, and rank the performance of the deployed models. The performance analysis revealed that ANFIS is the most effective model; hence, it is the central proposition of this study.

Original language	English
Title of host publication	17th International Conference on Developments in eSystems Engineering, DeSE 2024
Editors	Dhiya Al-Jumeily, Sulaf Assi, Manoj Jayabalan, Jade Hind, Abir Hussain, Hissam Tawfik, Neil Rowe, Jamila Mustafina
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	445-450
Number of pages	6
ISBN (Electronic)	9798350368697
DOIs	https://doi.org/10.1109/DeSE63988.2024.10912008
State	Published - 2024
Event	17th International Conference on Developments in eSystems Engineering, DeSE 2024 - Khorfakkan, United Arab Emirates Duration: 6 Nov 2024 → 8 Nov 2024

Publication series

Name	Proceedings - International Conference on Developments in eSystems Engineering, DeSE
ISSN (Print)	2161-1343

Conference

Conference	17th International Conference on Developments in eSystems Engineering, DeSE 2024
Country/Territory	United Arab Emirates
City	Khorfakkan
Period	6/11/24 → 8/11/24

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Adaptive neuro-fuzzy inference systems (ANFIS)
Artificial neural network (ANN)
Clinical Decision Support
Decision-making
Lung Cancer
Multiple linear regression (MLR)
fuzzy PROMETHEE

Access to Document

10.1109/DeSE63988.2024.10912008

Cite this

Onakpojeruo, E. P., Uzun, B., David, L. R., Ozsahin, I., Efe, C. C., & Ozsahin, D. U. (2024). Integrating ANFIS, ANN, & MLR with MCDM for Accurate Prediction of Lung Cancer for Improved Clinical Decision Support. In D. Al-Jumeily, S. Assi, M. Jayabalan, J. Hind, A. Hussain, H. Tawfik, N. Rowe, & J. Mustafina (Eds.), 17th International Conference on Developments in eSystems Engineering, DeSE 2024 (pp. 445-450). (Proceedings - International Conference on Developments in eSystems Engineering, DeSE). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DeSE63988.2024.10912008

Onakpojeruo, Efe Precious ; Uzun, Berna ; David, Leena R. et al. / Integrating ANFIS, ANN, & MLR with MCDM for Accurate Prediction of Lung Cancer for Improved Clinical Decision Support. 17th International Conference on Developments in eSystems Engineering, DeSE 2024. editor / Dhiya Al-Jumeily ; Sulaf Assi ; Manoj Jayabalan ; Jade Hind ; Abir Hussain ; Hissam Tawfik ; Neil Rowe ; Jamila Mustafina. Institute of Electrical and Electronics Engineers Inc., 2024. pp. 445-450 (Proceedings - International Conference on Developments in eSystems Engineering, DeSE).

@inproceedings{4e87d4245b5a4f9a8ae6b0ce1252d598,

title = "Integrating ANFIS, ANN, \& MLR with MCDM for Accurate Prediction of Lung Cancer for Improved Clinical Decision Support",

abstract = "The lungs regulate breathing and supply oxygen to every cell in the body. Simultaneously, they act as air filters, blocking potentially harmful particles and microbes from entering the respiratory system. Humans have built-in defences that keep their lungs safe. However, these cannot guarantee complete protection against lung diseases. The lungs are vulnerable to infection, inflammation, and malignant tumors. The aim of this research is to investigate the applications of three models, specifically an ANN, ANFIS, and a classical linear regression MLR for the prediction of lung cancer. To evaluate the models, we have considered four performance parameters: R2, MSE, RMSE, and R. To make the research robust, the study also deployed an MCDM tool called the fuzzy PROMETHEE to evaluate, compare, and rank the performance of the deployed models. The performance analysis revealed that ANFIS is the most effective model; hence, it is the central proposition of this study.",

keywords = "Adaptive neuro-fuzzy inference systems (ANFIS), Artificial neural network (ANN), Clinical Decision Support, Decision-making, Lung Cancer, Multiple linear regression (MLR), fuzzy PROMETHEE",

author = "Onakpojeruo, \{Efe Precious\} and Berna Uzun and David, \{Leena R.\} and Ilker Ozsahin and Efe, \{Christiana Chioma\} and Ozsahin, \{Dilber Uzun\}",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 17th International Conference on Developments in eSystems Engineering, DeSE 2024 ; Conference date: 06-11-2024 Through 08-11-2024",

year = "2024",

doi = "10.1109/DeSE63988.2024.10912008",

language = "English",

series = "Proceedings - International Conference on Developments in eSystems Engineering, DeSE",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "445--450",

editor = "Dhiya Al-Jumeily and Sulaf Assi and Manoj Jayabalan and Jade Hind and Abir Hussain and Hissam Tawfik and Neil Rowe and Jamila Mustafina",

booktitle = "17th International Conference on Developments in eSystems Engineering, DeSE 2024",

address = "United States",

}

Onakpojeruo, EP, Uzun, B, David, LR, Ozsahin, I, Efe, CC & Ozsahin, DU 2024, Integrating ANFIS, ANN, & MLR with MCDM for Accurate Prediction of Lung Cancer for Improved Clinical Decision Support. in D Al-Jumeily, S Assi, M Jayabalan, J Hind, A Hussain, H Tawfik, N Rowe & J Mustafina (eds), 17th International Conference on Developments in eSystems Engineering, DeSE 2024. Proceedings - International Conference on Developments in eSystems Engineering, DeSE, Institute of Electrical and Electronics Engineers Inc., pp. 445-450, 17th International Conference on Developments in eSystems Engineering, DeSE 2024, Khorfakkan, United Arab Emirates, 6/11/24. https://doi.org/10.1109/DeSE63988.2024.10912008

Integrating ANFIS, ANN, & MLR with MCDM for Accurate Prediction of Lung Cancer for Improved Clinical Decision Support. / Onakpojeruo, Efe Precious; Uzun, Berna; David, Leena R. et al.
17th International Conference on Developments in eSystems Engineering, DeSE 2024. ed. / Dhiya Al-Jumeily; Sulaf Assi; Manoj Jayabalan; Jade Hind; Abir Hussain; Hissam Tawfik; Neil Rowe; Jamila Mustafina. Institute of Electrical and Electronics Engineers Inc., 2024. p. 445-450 (Proceedings - International Conference on Developments in eSystems Engineering, DeSE).

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

TY - GEN

T1 - Integrating ANFIS, ANN, & MLR with MCDM for Accurate Prediction of Lung Cancer for Improved Clinical Decision Support

AU - Onakpojeruo, Efe Precious

AU - Uzun, Berna

AU - David, Leena R.

AU - Ozsahin, Ilker

AU - Efe, Christiana Chioma

AU - Ozsahin, Dilber Uzun

PY - 2024

Y1 - 2024

N2 - The lungs regulate breathing and supply oxygen to every cell in the body. Simultaneously, they act as air filters, blocking potentially harmful particles and microbes from entering the respiratory system. Humans have built-in defences that keep their lungs safe. However, these cannot guarantee complete protection against lung diseases. The lungs are vulnerable to infection, inflammation, and malignant tumors. The aim of this research is to investigate the applications of three models, specifically an ANN, ANFIS, and a classical linear regression MLR for the prediction of lung cancer. To evaluate the models, we have considered four performance parameters: R2, MSE, RMSE, and R. To make the research robust, the study also deployed an MCDM tool called the fuzzy PROMETHEE to evaluate, compare, and rank the performance of the deployed models. The performance analysis revealed that ANFIS is the most effective model; hence, it is the central proposition of this study.

AB - The lungs regulate breathing and supply oxygen to every cell in the body. Simultaneously, they act as air filters, blocking potentially harmful particles and microbes from entering the respiratory system. Humans have built-in defences that keep their lungs safe. However, these cannot guarantee complete protection against lung diseases. The lungs are vulnerable to infection, inflammation, and malignant tumors. The aim of this research is to investigate the applications of three models, specifically an ANN, ANFIS, and a classical linear regression MLR for the prediction of lung cancer. To evaluate the models, we have considered four performance parameters: R2, MSE, RMSE, and R. To make the research robust, the study also deployed an MCDM tool called the fuzzy PROMETHEE to evaluate, compare, and rank the performance of the deployed models. The performance analysis revealed that ANFIS is the most effective model; hence, it is the central proposition of this study.

KW - Adaptive neuro-fuzzy inference systems (ANFIS)

KW - Artificial neural network (ANN)

KW - Clinical Decision Support

KW - Decision-making

KW - Lung Cancer

KW - Multiple linear regression (MLR)

KW - fuzzy PROMETHEE

UR - https://www.scopus.com/pages/publications/105000468824

U2 - 10.1109/DeSE63988.2024.10912008

DO - 10.1109/DeSE63988.2024.10912008

M3 - Conference contribution

AN - SCOPUS:105000468824

T3 - Proceedings - International Conference on Developments in eSystems Engineering, DeSE

SP - 445

EP - 450

BT - 17th International Conference on Developments in eSystems Engineering, DeSE 2024

A2 - Al-Jumeily, Dhiya

A2 - Assi, Sulaf

A2 - Jayabalan, Manoj

A2 - Hind, Jade

A2 - Hussain, Abir

A2 - Tawfik, Hissam

A2 - Rowe, Neil

A2 - Mustafina, Jamila

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th International Conference on Developments in eSystems Engineering, DeSE 2024

Y2 - 6 November 2024 through 8 November 2024

ER -

Onakpojeruo EP, Uzun B, David LR, Ozsahin I, Efe CC, Ozsahin DU. Integrating ANFIS, ANN, & MLR with MCDM for Accurate Prediction of Lung Cancer for Improved Clinical Decision Support. In Al-Jumeily D, Assi S, Jayabalan M, Hind J, Hussain A, Tawfik H, Rowe N, Mustafina J, editors, 17th International Conference on Developments in eSystems Engineering, DeSE 2024. Institute of Electrical and Electronics Engineers Inc. 2024. p. 445-450. (Proceedings - International Conference on Developments in eSystems Engineering, DeSE). doi: 10.1109/DeSE63988.2024.10912008

Integrating ANFIS, ANN, & MLR with MCDM for Accurate Prediction of Lung Cancer for Improved Clinical Decision Support

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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