A Fuzzy Logic-Based Diagnostic Model for Lung Cancer Detection: An Improved Clinical Decision Support Framework
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Abstract
Lung disease, and lung cancer above all, remains one of the toughest problems in global health, taking a heavy toll each year because diagnosis so often arrives late or turns out to be wrong. The real clinical difficulty is not detection in general but catching disease at an early stage, when treatment has the best chance of working. Standard diagnostic methods tend to falter when symptom data is vague or overlapping in nature, which is exactly the situation fuzzy logic is built to handle: it reasons under uncertainty while still giving clinicians an interpretable output. This paper reports a full study built around five linked goals. First, the relevant diagnostic parameters for selected lung diseases were gathered and consolidated from the clinical literature. Second, a new Mamdani-type fuzzy inference system was designed, with membership functions chosen specifically for the parameters identified. Third, established fuzzy diagnostic models from the literature were re-implemented and run against real patient data so their reported performance could be checked rather than simply cited. Fourth, the newly proposed model was tested under the same conditions. Fifth, all of the models were placed side by side in a structured comparison that looks beyond raw accuracy to include interpretability, execution time, confidence output, and treatment guidance. Testing on the benchmark UCI lung cancer dataset shows the proposed model reaching a mean accuracy of 97.10 percent under 10-fold cross-validation, without discarding any of the original features through dimensionality reduction, and producing confidence values that carry genuine clinical meaning. Taken together, the work delivers both a working diagnostic model and a comparison framework intended to steer future research in intelligent, fuzzy-logic-based healthcare decision support....
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