Application of Fuzzy Logic in Medicine - Zubir, Nazira; Pushpanathan, Kalananthni (2016)
Lack of information, imprecision, contradictory nature are common facts in medicine. Generally, the diagnosis of disease involves several levels of uncertainty and imprecision. A single disease may be exhibited quite distinctively, depending on the patient, and with different intensities. A single symptom may correspond to different diseases while several diseases present in a patient may interact and interfere with the usual description of any of the diseases. The best and most precise description of disease entities uses linguistic terms that are also imprecise and vague. Hence, in dealing with imprecision and uncertainty, fuzzy logic is most preferred. Fuzzy logic introduces partial truth values, between true and false and encompasses the theory and application of fuzzy sets and fuzzy logic. Fuzzy logic can be incorporated in the information processing, systems analysis and problems synthesis. The spectacular growth of fuzzy computation in the recent years has led to major applications in medical field for instance in the interpretation, reporting and optimizing the decision making of the medical imaging images when diagnosing certain diseases. The image mining in particular is gaining new momentum among health practitioners and researchers for example to improve diagnostic accuracy, identify high risk patients and track concepts from unstructured data. The integration of Mamdani Fuzzy Inference System in the image processing and classification techniques for diagnosing breast cancer and detecting high risk of breast cancer development is further elaborated. The similar techniques can be adopted to diagnose other types of cancer for examples lung and brain cancers.