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Implementation of Adaptive Neuro Fuzzy Inference System for Malaria Diagnosis (Case Study: Kwesimintsim Polyclinic)
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 115 - Number 7 |
| Year of Publication: 2015 |
| Authors: Richard Appiah, Joseph Kobina Panford, Kwabena Riverson |
10.5120/20166-2284
|
Richard Appiah, Joseph Kobina Panford, Kwabena Riverson . Implementation of Adaptive Neuro Fuzzy Inference System for Malaria Diagnosis (Case Study: Kwesimintsim Polyclinic). International Journal of Computer Applications. 115, 7 ( April 2015), 33-37. DOI=10.5120/20166-2284
Abstract
Health issues have become one of the problems bedeviling most developing and under-developed countries in our world today. Ghana is of no exception from this menace especially in Africa. One of the prevalent diseases battling with Ghanaians and Africa as a whole is the malaria disease. In 1994, the WHO reported that malaria and measles were the most common causes of premature death. in children under five(5) years. Diagnosis of malaria in many cases has not been accurate by most doctors or physicians due to external human factors such as fatigue and hastiness among others, thereby leading to patients being subjected to treatment again which also come with cost. This paper employs the use of Adaptive Neuro-Fuzzy Inference System (ANFIS) to provide a better option for malaria diagnosis than the traditional diagnosis method which is characterized by erotic guess work and observation of patients by doctors. Datasets of patients divided into training and checking data were used to train the ANFIS. The results tested after training showed that ANFIS has the ability to diagnose malaria efficiently than the traditional method with very minimal error.
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