Malavefes: A computational voice-enabled malaria fuzzy informatics software for correct dosage prescription of anti-malarial drugs

Malaria is one of the infectious diseases consistently inherent in many Sub-Sahara African countries. Among the issues of concern are the consequences of wrong diagnosis and dosage administration of anti-malarial drugs on sick patients; these have resulted into various degrees of complications ranging from severe headaches, stomach and body discomfort, blurred vision, dizziness, hallucinations, and in extreme cases, death. Many expert systems have been developed to support different infectious disease diagnoses, but not sure of any yet, that have been specifically designed as a voice-based application to diagnose and translate malaria patients' symptomatic data for pre-laboratory screening and correct prescription of proper dosage of the appropriate medication. We developed Malavefes, (a malaria voice-enabled computational fuzzy expert system for correct dosage prescription of anti-malarial drugs) using Visual Basic.NET., and Java programming languages. Data collation for this research was conducted by survey from existing literature and interview from public health experts. The database for this malaria drug informatics system was implemented using Microsoft Access. The Root Sum Square (RSS) was implemented as the inference engine of Malavefes to make inferences from rules, while Centre of Gravity (CoG) was implemented as the defuzzification engine. The drug recommendation module was voice-enabled. Additional anti-malaria drug expiration validation software was developed using Java programming language. We conducted a user-evaluation of the performance and user-experience of the Malavefes software.