An Expert System for Malaria Diagnosis using the Fuzzy Cognitive Map Engine
Bibliographic record
Abstract
The complexity of malaria diagnosis increases because of symptom manifestation that could be confused with other tropical diseases and the fuzziness associated with patients’ expression of their health conditions. There is a need for appropriate diagnostic tools that would assist the physician (or other trained medical personnel) in the differential diagnosis of malaria and other tropical diseases. In this paper, we present an initial result of an effort to develop a fuzzy cognitive map (FCM) system for the diagnosis of malaria. Concepts and their causality were defined based on the experiential knowledge from 30 physicians in 3 hospitals in Nigeria, who served as knowledge sources for this study. The semantic relationships among concepts were utilized in constructing an FCM model for malaria diagnosis, which was further integrated into a decision support engine (DSE). The comparative summary showed that the initial hypotheses (IH) by the physicians correctly matched the final diagnosis in 55% of the cases, whereas the accurate diagnosis (AD) of the FCM was 85%. This result is interesting; further analysis using Kendall’s tau_b and the Spearman’s rank order test also indicated a higher (though equally significant) correlation between the FCM results and AD than between IH and AD. The correlation between the physician’s initial hypothesis and the FCM diagnosis was not significant.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".