A new hybrid model of dengue incidence rate using negative binomial generalised additive model and fuzzy c-means model: a case study in Selangor

Dengue is one of the top reason for illness and mortality in the world with beyond one­third of the world's population living in the risk areas of dengue infection. In this study, there are five stages to achieve the research objectives. Firstly, the verification of predetem1ined variables. Secondly, the identification of new datasets after clustered by district and Fuzzy C-Means Model (FCM). Thirdly, the development of models using the existing dataset and the new datasets which clustered by the two different clustering categories. Then, to assess the models developed by using three measurement methods which are deviance (D), Akaike Jnfonnation Criteria (AIC) and Bayesian Infonnation Criteria (BIC} Lastly, the validation of model developed by comparing the value of D, AIC and BIC between the existing model and the new models developed which used the new datasets. There are two different clustering techniques applied which are clustering the data by district and by FCM. This study proposed a new modelling hybrid framework by using two statistical models which are FCM and negative binomial Generalised Additive Model (GAM). This study successfully presents the significant difference in the climatic and non-climatic factors that influenced dengue incidence rate (DIR) in Selangor, Malaysia. Results show that the climatic factors such as rainfall with current month up to 3 months and number of rainy days with current month up to lag 3 months are significant to DIR. Besides, the interaction between rainfall and number of rainy days also shows strong positive relationship to DIR. Meanwhile, non-climatic vaiiables such as population density, number of locality and lag DIR from I month until 3 months also show significant relationship towards DIR For both clustering techniques, there are two clusters fonned and there are four new models developed in this study. After comparing the values of D, AIC ai1d BIC between the existing model and the new models, this study concluded that four new models recorded lower values compared to the existing model. Therefore, the four new models are selected to present the dengue incidence in Selangor

A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income

Analysis by human perception could not be solved using traditional method since uncertainty within the data have to be dealt with first. Thus, fuzzy structure system is considered. The objectives of this study are to determine suitable cluster by using fuzzy c-means (FCM) method, to apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression (FLR) as proposed by Tanaka and Ni and to improve the FCM method and FLR model proposed by Zolfaghari to predict manufacturing income. This study focused on FLR which is suitable for ambiguous data in modelling. Clustering is used to cluster or group the data according to its similarity where FCM is the best method. The performance of models will measure by using the mean square error (MSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE). Results shows that the improvisation of FCM method and FLR model obtained the lowest value of error measurement with MSE=1.825 11 10 , MAE=115932.702 and MAPE=95.0366. Therefore, as the conclusion, a new hybrid of FCM method and FLR model are the best model for predicting manufacturing income compared to the other model

Application FCM in modelling DIR for Selangor using negative binomial GAM

This study attempts to obtain the best fitted model among two clusters which describe the relationship between dengue incidence rate (DIR) and relevant covariates such as climatic and non-climatic variables. The significant variables include amount of rainfall and number of rainy days with lag 0 until 3 months, number of locality and population density. Fuzzy C-Means clustering (FCM) was applied in clustering DIR data based on the value of membership function. The boundary of membership function has been set as 0.5. There are two clusters identified in this study with Cluster 1 consist of 569 data and Cluster 2 consist of 43 data. Then, this study developed models to predict future dengue incidences in Selangor by using negative binomial Generalised Additive Model (GAM). The result shows that the model able to be one of tools for future development in controlling and reducing the number of dengue cases particularly in Selangor, Malaysia as well as other states

A new hybrid model of dengue incidence rate using negative binomial generalised additive model and fuzzy C means model a case study in Selangor

Dengue is one of the top reason for illness and mortality in the world with beyond one-third of the world’s population living in the risk areas of dengue infection. In this study, there are five stages to achieve the research objectives. Firstly, the verification of predetermined variables. Secondly, the identification of new datasets after clustered by district and Fuzzy C-Means Model (FCM). Thirdly, the development of models using the existing dataset and the new datasets which clustered by the two different clustering categories. Then, to assess the models developed by using three measurement methods which are deviance (D), Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). Lastly, the validation of model developed by comparing the value of D, AIC and BIC between the existing model and the new models developed which used the new datasets. There are two different clustering techniques applied which are clustering the data by district and by FCM. This study proposed a new modelling hybrid framework by using two statistical models which are FCM and negative binomial Generalised Additive Model (GAM). This study successfully presents the significant difference in the climatic and non-climatic factors that influenced dengue incidence rate (DIR) in Selangor, Malaysia. Results show that the climatic factors such as rainfall with current month up to 3 months and number of rainy days with current month up to lag 3 months are significant to DIR. Besides, the interaction between rainfall and number of rainy days also shows strong positive relationship to DIR. Meanwhile, non-climatic variables such as population density, number of locality and lag DIR from 1 month until 3 months also show significant relationship towards DIR. For both clustering techniques, there are two clusters formed and there are four new models developed in this study. After comparing the values of D, AIC and BIC between the existing model and the new models, this study concluded that four new models recorded lower values compared to the existing model. Therefore, the four new models are selected to present the dengue incidence in Selangor

A new hybrid model of dengue incidence rate using negative binomial generalised additive model and fuzzy C means model a case study in Selangor

Dengue is one of the top reason for illness and mortality in the world with beyond one-third of the world’s population living in the risk areas of dengue infection. In this study, there are five stages to achieve the research objectives. Firstly, the verification of predetermined variables. Secondly, the identification of new datasets after clustered by district and Fuzzy C-Means Model (FCM). Thirdly, the development of models using the existing dataset and the new datasets which clustered by the two different clustering categories. Then, to assess the models developed by using three measurement methods which are deviance (D), Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). Lastly, the validation of model developed by comparing the value of D, AIC and BIC between the existing model and the new models developed which used the new datasets. There are two different clustering techniques applied which are clustering the data by district and by FCM. This study proposed a new modelling hybrid framework by using two statistical models which are FCM and negative binomial Generalised Additive Model (GAM). This study successfully presents the significant difference in the climatic and non-climatic factors that influenced dengue incidence rate (DIR) in Selangor, Malaysia. Results show that the climatic factors such as rainfall with current month up to 3 months and number of rainy days with current month up to lag 3 months are significant to DIR. Besides, the interaction between rainfall and number of rainy days also shows strong positive relationship to DIR. Meanwhile, non-climatic variables such as population density, number of locality and lag DIR from 1 month until 3 months also show significant relationship towards DIR. For both clustering techniques, there are two clusters formed and there are four new models developed in this study. After comparing the values of D, AIC and BIC between the existing model and the new models, this study concluded that four new models recorded lower values compared to the existing model. Therefore, the four new models are selected to present the dengue incidence in Selangor

Dengue incidence rate clustering by district in Selangor

This study presents the used of Generalised Additive Model (GAM) in modelling Dengue Incidence Rate (DIR) with adopted clustering technique for districts in Selangor. This study identified a pattern for monthly observed dengue count and successfully select variables includes number of rainy days and amount of rainfall with time lags, number of locality and population density which significant to DIR in Selangor. Besides, this study found the districts divided into two clusters based on the value of mean DIR from January 2010 to August 2015. The first cluster consist of 6 districts of Selangor with value of mean DIR from 0 to 200 cases per 100,000 population. Meanwhile, there are 3 districts classified in the second cluster with value of mean DIR from 200 to 500 cases per 100,000 population. The Negative Binomial GAM then adopted in this study to able to handle the presence of overdispersion. In conclusion, clustering technique is one of the effective techniques to identify the different district with the higher potential of dengue risk

Prediction in a hybrid of fuzzy linear regression with symmetric parameter model and fuzzy c-means method using simulation data

The objective of fuzzy linear regression model (FLRM) to predict the dependent variable and independent variables in vague phenomenon. In this study, several models such as fuzzy linear regression model (FLRM), fuzzy linear regression with symmetric parameter (FLWSP) and a hybrid model have been applied to be evaluated by 1000 rows in 1 simulation data. Moreover, the hybrid method was applied between fuzzy linear regression with symmetric parameter (FLRWSP) and fuzzy c-mean (FCM) method to get the effective prediction in a new model and best result in this study. To improve the accuracy of evaluating and predicting, this study employ two measurement error of cross validation statistical technique which are mean square error (MSE) and root mean square error (RMSE). The simulation result suggests that comparison among models using two measurement errors should be to determine the best results. Finally, this study notes that the new hybrid model of FLRWSP and FCM is verified to be a good model with the least value of MSE and RMSE measurement errors