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Fuzzy Expert System for Malaria Diagnosis


Affiliations
1 Mathematics and Computer Science Department, Delta State University, Abraka, Nigeria
 

Malaria remains one of the world’s most deadly infectious diseases and arguably, the greatest menace to modern society in terms of morbidity and mortality. To choose the right treatment and to ensure a quality of life suitable for a specific patient condition, early and accurate diagnosis of malaria is essential. It reduces disease and prevents deaths. It also contributes to reducing malaria transmission. In recent times, Information Technology (IT) has played a significant role in the task of medical diagnosis. This paper work focused on Fuzzy Expert System for malaria diagnosis. It is simple to use, portable, low cost and makes malaria diagnosis more rapid and accurate. It supports medical practitioners and assists malaria researchers to deal with the vagueness, imprecision and time-consuming found in traditional laboratory diagnosis of malaria, and provide accurate output based on the input data.

Keywords

Fuzzy Logic, Expert Systems, Malaria, Symptoms.
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  • Fuzzy Expert System for Malaria Diagnosis

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Authors

Ojeme Blessing Onuwa
Mathematics and Computer Science Department, Delta State University, Abraka, Nigeria

Abstract


Malaria remains one of the world’s most deadly infectious diseases and arguably, the greatest menace to modern society in terms of morbidity and mortality. To choose the right treatment and to ensure a quality of life suitable for a specific patient condition, early and accurate diagnosis of malaria is essential. It reduces disease and prevents deaths. It also contributes to reducing malaria transmission. In recent times, Information Technology (IT) has played a significant role in the task of medical diagnosis. This paper work focused on Fuzzy Expert System for malaria diagnosis. It is simple to use, portable, low cost and makes malaria diagnosis more rapid and accurate. It supports medical practitioners and assists malaria researchers to deal with the vagueness, imprecision and time-consuming found in traditional laboratory diagnosis of malaria, and provide accurate output based on the input data.

Keywords


Fuzzy Logic, Expert Systems, Malaria, Symptoms.