This paper argues that advances in fuzzy reasoning systems in healthcare are critical for patient data, requiring innovative methodologies that combine the interpretive capabilities of artificial intelligence with robust resolution of inherent uncertainty. Confusion often arises in healthcare settings due to the variability of patient conditions, diagnostic test results, and the dynamic nature of diseases. Healthcare systems are challenged to manage the uncertainty inherent in patient data, which requires the use of sophisticated decision-making tools such as fuzzy logic systems (FTS) to account for uncertainty.
| Mualliflar | Примова, Х., Вайдуллаева, М. |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashr sanasi | 2025-09-30 |
| Jild | 8 |
| Son | 3 |
| Betlar | 123-129 |
| Til | Rus |
| DOI | 10.62132/ijdt.v8i3.295 |
DOI: 10.62132/ijdt.v8i3.295 · Maqolaning asl sahifasi
нечеткие системы мышления, искусственный интеллект, гауссовские функции принадлежности, нечеткая кластеризация C-средних, функция принадлежности, fuzzy reasoning systems, artificial intelligence, Gaussian membership functions, fuzzy C-means clustering, membership function
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