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May 28, 2026American Journal of Mathematical and Computer ModellingOpen Access

Fuzzy Logic Based Model for Predicting Neurasthenia in Nigerian University Students

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Authors

IIIyinoluwa IdowuEAEmmanuel AyodeleSFSholanke Folasade

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Overview

Randomized trial develops a fuzzy logic model predicting neurasthenia risk in university students, suggesting tools for early detection.

Key Points

  • The aim is to develop a fuzzy logic-based model to estimate the likelihood of neurasthenia among students using interpretable factors.
  • Developed a predictive model using MATLAB Fuzzy Logic Toolbox (R2024b).
  • Identified risk factors through literature and expert consultation, mapping them to linguistic terms and membership functions.
  • Simulated a system with six input variables to classify likelihood across low, moderate, and high levels.
  • 25% of cases classified as low likelihood, 40% as moderate likelihood, and 35% as high likelihood of neurasthenia.
  • High academic stress, poor sleep, frequent stimulant use, and severe emotional distress led to high likelihood outputs.
  • Positive factors such as good sleep and effective study habits resulted in low likelihood of neurasthenia.

Cite This Study

Idowu et al. (2026) studied this question.

synapsesocial.com/papers/6a17dbbe3fad632b0f9d87bahttps://doi.org/10.11648/j.ajmcm.20261102.12
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