Artificial Intelligence System for Malaria Diagnosis

Phoebe A. Barracloug, Charles M. Were, Hilda Mwangakala, Gerhard Fehringer, Dornald O. Ohanya, Harison Agola, Philip Nandi

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Malaria threats have remained one of the major global health issues over the past decades specifically in low-middle 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.

Original languageEnglish
Pages (from-to)920-932
Number of pages13
JournalInternational Journal of Advanced Computer Science and Applications
Issue number3
Publication statusPublished - 30 Mar 2024

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