The Fuzzy Linear Regression

Al-Sabri, Esmail Hassan Abdullatif

doi:10.28924/apjm/7-7

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…The fuzzy linear regression approach is simpler and more transparent to calculate than classical regression but does not significantly differ from classical regression. Furthermore, these results provide support for the concept of fuzzy linear regression prediction, especially when it comes to fuzzy data [24]. The high-risk symptoms of lung cancer can be detected with greater precision by using the fuzzy linear regression method, which provides a better prediction of imprecise data than regression analysis.…”

Section: Methodssupporting

confidence: 52%

The Use of Fuzzy Linear Regression Modeling to Predict High-risk Symptoms of Lung Cancer in Malaysia

Zakaria¹,

Shafi²,

Zim³

et al. 2023

IJACSA

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Lung cancer is the most prevalent cancer in the world, accounting for 12.2% of all newly diagnosed cases in 2020 and has the highest mortality rate due to its late diagnosis and poor symptom detection. Currently, there are 4,319 lung cancer deaths in Malaysia, representing 2.57 percent of all mortality in 2020. The late diagnosis of lung cancer is common, which makes survival more difficult. In Malaysia, however, most cases are detected when the tumors have become too large, or cancer has spread to other body areas that cannot be removed surgically. This is a frequent situation due to the lack of public awareness among Malaysians regarding cancer-related symptoms. Malaysians must be acknowledged the highrisk symptoms of lung cancer to enhance the survival rate and reduce the mortality rate. This study aims to use a fuzzy linear regression model with heights of triangular fuzzy by Tanaka (1982), H-value ranging from 0.0 to 1.0, to predict high-risk symptoms of lung cancer in Malaysia. The secondary data is analyzed using the fuzzy linear regression model by collecting data from patients with lung cancer at Al-Sultan Abdullah Hospital (UiTM Hospital), Selangor. The results found that haemoptysis and chest pain has been proven to be the highest risk, among other symptoms obtained from the data analysis. It has been discovered that the H-value of 0.0 has the least measurement error, with mean square error (MSE) and root mean square error (RMSE) values of 1.455 and 1.206, respectively.

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Section: Methodssupporting

confidence: 52%