Combination of Adaptive Fuzzy Inference System and Simulated Annealing Algorithm-based for Malaria Susceptibility Mapping in Daknong Province
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Abstract
Abstract: Adaptive Neuro-Inference system (Anfis) has been widely used in recent studies aiming at generating probabilities of unseen data in binary classification application. It is normally used in combination with optimization algorithms for tuning its parameters to generate optimal objective values. This study proposed a state-of-the-art method using Simulated Annealing to improve Anfis performance. Malaria occurrences and spatial variation of environmental, socio-economic factors in Daknong province, Vietnam were selected for case study. For accuracy assessment, Receiver Operating Characteristic curve, Cost curve were used and the predicted map was compared to several benchmark classifiers. The results showed that the S-Anfis (AUC = 0.912, RMSE =0.335) outperformed Support Vector Machine (AUC = 0.902, RMSE =0.364), Multiple Layer Perceptron (AUC = 0.868, RMSE =0.430). Although, the performance of S-Anfis depended on proper selection of input factors and geographic variations of those, we concluded that this method could be an alternative in mapping susceptibility of malaria.
Keywords: Anfis, Simulated annealing, malaria.
References
[1] WHO, World Malaria Report 2016, Geneva, 2016.
[2] M. M. Ndiath et al., “Application of geographically-weighted regression analysis to assess risk factors for malaria hotspots in Keur Soce health and demographic surveillance site,” Malaria Journal, vol. 14, pp. 463, 11/18
[3] Q.-T. Bui et al., “Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers,” Geocarto International, pp. 1-15, 2018.
[4] Y. Ge et al., “Geographically weighted regression-based determinants of malaria incidences in northern China,” Transactions in GIS, pp. n/a-n/a, 2016.
[5] N. Metropolis et al., “Equation of State Calculations by Fast Computing Machines,” The Journal of Chemical Physics, vol. 21, no. 6, pp. 1087-1092, 1953/06/01, 1953.
[6] N. Mathur, I. Glesk, and A. Buis, “Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses,” Medical Engineering & Physics, vol. 38, no. 10, pp. 1083-1089, 2016/10/01/, 2016.
[7] D. Tien Bui et al., “A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area,” Agricultural and Forest Meteorology, vol. 233, pp. 32-44, 2017/02/15/, 2017.
[8] D. Tien Bui et al., “GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks,” Environmental Earth Sciences, vol. 75, no. 14, pp. 1-22, 2016.
References
[1] WHO, World Malaria Report 2016, Geneva, 2016.
[2] M. M. Ndiath et al., “Application of geographically-weighted regression analysis to assess risk factors for malaria hotspots in Keur Soce health and demographic surveillance site,” Malaria Journal, vol. 14, pp. 463, 11/18
[3] Q.-T. Bui et al., “Understanding spatial variations of malaria in Vietnam using remotely sensed data integrated into GIS and machine learning classifiers,” Geocarto International, pp. 1-15, 2018.
[4] Y. Ge et al., “Geographically weighted regression-based determinants of malaria incidences in northern China,” Transactions in GIS, pp. n/a-n/a, 2016.
[5] N. Metropolis et al., “Equation of State Calculations by Fast Computing Machines,” The Journal of Chemical Physics, vol. 21, no. 6, pp. 1087-1092, 1953/06/01, 1953.
[6] N. Mathur, I. Glesk, and A. Buis, “Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses,” Medical Engineering & Physics, vol. 38, no. 10, pp. 1083-1089, 2016/10/01/, 2016.
[7] D. Tien Bui et al., “A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area,” Agricultural and Forest Meteorology, vol. 233, pp. 32-44, 2017/02/15/, 2017.
[8] D. Tien Bui et al., “GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks,” Environmental Earth Sciences, vol. 75, no. 14, pp. 1-22, 2016.