Hybridized Support Vector Machineartificial Neural Network Malaria Prediction Model

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The increase in the rate of malaria infections called for more rigorous research efforts. Evidences have shown that complexities involved in malaria forecasting required computational approach. Most existing conventional malaria forecasting models examine the dynamics of binary cases of asymptomatic of malaria parasite counts in the thick blood smear. Such existing models are plagued with the problem of over-fitting and prone to local minimum error due to large number of parameters to fix. Therefore, this study aimed at developing hybridized malaria prediction model for a multiclass nature of malaria parasite counts with effect of climatic conditions, given a non-linear malaria incidence cases. The objectives were to: (i) identify the factors that make an individual susceptible to malaria infection and the threats imposed; (ii) determine likelihood qualitative model of Artificial Neural Network (ANN) and Support Vector Machine (SVM); (iii) develop a thresholded hybridized SVM_ANN malaria model for better performance; and (iv) simulate the model to evaluate its performance compared with existing models using classification accuracy ( , sensitivity , specificity and mean square error (mse). rnMonthly survey of malarial incidence was collected from five randomly sampled health centers in Minna Metropolis, Niger State, Nigeria. Climatic data was also collected from the Nigerian Environmental and Climate Observation Programme (NECOP) Weather Station, Nigeria. These served as the model input variables. ANN with sigmoid transfer function for classification and SVM with radial basis function for feature selection were employed to predict the severity of multiclass malaria parasite counts.rnThe findings of the study were that:rni. the internal factors of frequency of human blood index, duration of sporogony, vector density, vector susceptibility, demography and external factors of temperature, rainfall, relative humidity vegetation, altitude, human behavioural factors and other environmental changes contribute to the possibilities of asymptomatic, symptomatic and climatic based threats;rnii. SVM feature selection method produced optimal features for ANN classification;rniii. hybridized SVM_ANN malaria predicting model was developed at an optimum threshold 0.60 and functionally expressed asrnrnSVM_ANN model = ϕ ( )rnrnwhere ϕ (x) is the transfer function, = , ,xs, xc are support vectors with constraints of langrange multipliers αs, αc> 0 and αs, αc ∈αi and targets yi∈[0,1]; andrnrniv. SVM_ANN hybridized malaria model achieved a better 98.91% , 100% , 98.68% and 0.14 mse compared with existing ANN and SVM model with 48.33%, 85.60% ; 60.61%, 84.06% ; 45.58%, 86.09% and 0.56 , 0.58 mse respectively.rnrnThe study concluded that the prediction of multiclass symptomatic malaria infection with effects of climatic conditions using SVM_ANN hybridized model was established to be more efficient than existing models. Thus, this study recommended that the SVM_ANN hybridized model should be adopted by medical personnel and stakeholders to predict malaria incidence occurrences and its severity.

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Hybridized Support Vector Machineartificial Neural Network Malaria Prediction Model

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