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- Title
Artificial Intelligence System for Malaria Diagnosis.
- Authors
Barracloug, Phoebe A.; Were, Charles M.; Mwangakala, Hilda; Fehringer, Gerhard; Ohanya, Dornald O.; Agola, Harison; Nandi, Philip
- Abstract
Malaria threats have remained one of the major global health issues over the past decades specifically in lowmiddle income countries. 70% of the Kenya population lives in malaria endemic zones and the majority have barriers to access health services due to factors including lack of income, distance, and social culture. Despite various research efforts using blood smears under a microscope to combat malaria with advantages, this method is time consuming and needs skillful personnel. To effectively solve this issue, this study introduces a new method integrating InfoGainAttributeEval feature selection techniques and parameter tuning method based on Artificial Intelligence and Machine Learning (AIML) classifiers with features to diagnose types of malaria more accurately. The proposed method uses 100 features extracted from 4000 samples. Sets of experiments were conducted using Artificial Neural Network (ANNs), Naïve Bayes (NB), Random Forest (RF) classifiers and Ensemble methods (Meta Bagging, Random Committee Meta, and Voting). Naïve Bayes has the best result. It achieved 100% accuracy and built the model in 0.01 second. The results demonstrate that the proposed method can classify malaria types accurately and has the best result compared to the reported results in the field.
- Subjects
MALARIA diagnosis; ARTIFICIAL intelligence; MACHINE theory; PROTOZOAN diseases; LOW-income countries; MIDDLE-income countries
- Publication
International Journal of Advanced Computer Science & Applications, 2024, Vol 15, Issue 3, p920
- ISSN
2158-107X
- Publication type
Article
- DOI
10.14569/ijacsa.2024.0150392