Determinants status of patient after receiving treatment at Intensive Care Unit: A case study in Johor Bahru

Shafi, Muhammad Ammar; Rusiman, Mohd Saifullah; Yusof, Nur Syaliza Hanim Che

doi:10.1109/i4ct.2014.6914150

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Recently, applied linear statistical models have been used in many fields such as medication, economy, social science and many more [1,11,12]; [1][2][3][4][5][6][7][8][9][10][11][12]. The main objective of linear statistical analysis is to predict the relationship of a respondent variable in terms of predictor variables in a linear function or multiple linear functions.…”

Section: Introductionmentioning

confidence: 99%

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Application of Fuzzy Linear Regression with Symmetric Parameter for Predicting Tumor Size of Colorectal Cancer

Shafi¹,

Rusiman²,

Abdullah³

2021

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The colon and rectum is the final portion of the digestive tube in the human body. Colorectal cancer (CRC) occurs due to bacteria produced from undigested food in the body. However, factors and symptoms needed to predict tumor size of colorectal cancer are still ambiguous. The problem of using linear regression arises with the use of uncertain and imprecise data. Since the fuzzy set theory's concept can deal with data not to a precise point value (uncertainty data), this study applied the latest fuzzy linear regression to predict tumor size of CRC. Other than that, the parameter, error and explanation for the both models were included. Furthermore, secondary data of 180 colorectal cancer patients who received treatment in general hospital with twenty five independent variables with different combination of variable types were considered to find the best models to predict the tumor size of CRC. Two models; fuzzy linear regression (FLR) and fuzzy linear regression with symmetric parameter (FLRWSP) were compared to get the best model in predicting tumor size of colorectal cancer using two measurement statistical errors. FLRWSP was found to be the best model with least value of mean square error (MSE) and root mean square error (RMSE) followed by the methodology stated.

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Section: Introductionmentioning

confidence: 99%

“…The FLR and FLRWSP model have been applied to a colorectal cancer data. The comparison among both models was done based on measurement error values such as MSE and RMSE [7,8,9,10]; [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Linear Regression with Symmetric Parameter for Predicting Tumor Size of Colorectal Cancer

Shafi¹,

Rusiman²,

Abdullah³

2021

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“…With the house price model, it will contribute important information to the real estate market which will indirectly influence the economic growth of the country [4]. Multicollinearity is commonly occur in the high dimensional data where it will give a serious problem whenever analysis using multiple regression [5,6,7]. Therefore, Ridge regression and Lasso regression can be used to cope the multicollinearity problem [8,9].…”

Section: Introductionmentioning

confidence: 99%

Modelling House Price Using Ridge Regression and Lasso Regression

Xin¹,

Khalid²

2018

IJET

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House price prediction is important for the government, finance company, real estate sector and also the house owner. The data of the house price at Ames, Iowa in United State which from the year 2006 to 2010 is used for multivariate analysis. However, multicollinearity is commonly occurred in the multivariate analysis and gives a serious effect to the model. Therefore, in this study investigates the performance of the Ridge regression model and Lasso regression model as both regressions can deal with multicollinearity. Ridge regression model and Lasso regression model are constructed and compared. The root mean square error (RMSE) and adjusted R-squared are used to evaluate the performance of the models. This comparative study found that the Lasso regression model is performing better compared to the Ridge regression model. Based on this analysis, the selected variables includes the aspect of house size, age of house, condition of house and also the location of the house.

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The use of fuzzy linear regression models for tumor size in colorectal cancer in hospital of Malaysia

Shafi¹,

Rusiman²

2015

ams

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Regression analysis has become popular among several fields of research and standard tools in analysing data. This structure was represented by four commonly statistical models such as multiple linear regression, fuzzy linear regression (Tanaka, 1982), fuzzy linear regression (Ni, 2005) and extended fuzzy linear regression by benchmarking models under fuzziness (Chung, 2012). Colorectal cancer (CRC) was applied on CRC cases in Malaysia. The CRC patients' quality of life in order to detect the CRC at an early stage is still very poor, the programmes are mainly ad-hoc and not implemented as a national wide programme. This study aims to determine the best model to measure the tumor size at hospitals using mean square error and root mean square error. Secondary data was used where 180 patients having colorectal cancer and receiving treatment in hospitals was recorded by nurses and doctors. Based on the results, fuzzy linear regression (Ni, 2005) is the best model to predict the tumor size developed by patients after receiving treatment in hospital.

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Determinants status of patient after receiving treatment at Intensive Care Unit: A case study in Johor Bahru

Cited by 5 publications

References 5 publications

Application of Fuzzy Linear Regression with Symmetric Parameter for Predicting Tumor Size of Colorectal Cancer

Application of Fuzzy Linear Regression with Symmetric Parameter for Predicting Tumor Size of Colorectal Cancer

Modelling House Price Using Ridge Regression and Lasso Regression

The use of fuzzy linear regression models for tumor size in colorectal cancer in hospital of Malaysia

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