Machine Learning and Data Mining Methods in Early Detection of Stomach Cancer Risk

Md. Rejaul Islam  Royel; Md. Ajmanur  Jaman; Fuyad  Al Masud; Arzo  Ahmed; Abdul  Muyeed; Kawsar  Ahmed

doi:10.6180/jase.202102_24(1).0001

Machine Learning and Data Mining Methods in Early Detection of Stomach Cancer Risk

Md. Rejaul Islam Royel¹ , Md. Ajmanur Jaman¹ , Fuyad Al Masud^1,2 , Arzo Ahmed³ , Abdul Muyeed⁴ , and Kawsar Ahmed This email address is being protected from spambots. You need JavaScript enabled to view it.^2,5

¹Department of Software Engineering (SWE), Daffodil International University, Dhaka, Bangladesh
²Department of Information and Communication Technology (ICT), Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh
³Department of Statistics, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh
⁴Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Bangladesh
⁵Group of Bio-photomatix, Department of Information and Communication Technology (ICT), Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh

Received: May 1, 2020
Accepted: July 7, 2020
Publication Date: February 1, 2021

Copyright The Author's institute. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202102_24(1).0001

ABSTRACT

Background: In point of mortality rate, stomach cancer is the fifth leading cancer. There are some risk factors of stomach cancer those are varied with country to country and associated with urbanization and economic development. Diagnosis of stomach cancer is a difficult task, only about 10% of people are diagnosed while it’s still in the initial stage. The main objective of this research is to design a tool for early detection of stomach cancer risk level. Methodology: Firstly, feature selection techniques are applied to filter the collected data. After that, the best rules technique is used to check the correlations of risk factors with stomach cancer. Besides, the visual relationship among factors and selected cancer are also exhibited. Then, the score is assigned for each factor according to the impact of risk on stomach cancer patients. Finally, the stomach cancer risk level prediction tool is designed. Results: After the experiment of 300 subjects’ records (150 are affected and 150 are non-affected) with 32 risk factors, we have received 18 significant-top risk factors of stomach cancer. Abdominal Pain, Nausea, Skin Color Turn into pale are respectively found top risk factors of stomach cancer. Furthermore, some other factors related to socio-economic conditions are also indicated to have stomach cancer. Conclusion: In conclusion, this study will be helpful to early detection of stomach cancer risk level and to increase the awareness among the people of Bangladesh as well as the rest of the world.

Keywords: Stomach Cancer in Bangladesh; Preoperative Risk Factors; Feature Selection Method; Risk algorithm; Correlation

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