This study examines the mathematical modeling methods for the prevention and early diagnosis of endocrine diseases. The research applies probability theory, statistical analysis, classification algorithms, and clustering methods to develop approaches for optimizing the diagnostic process. The results indicate that mathematical models play a significant role in improving diagnostic accuracy and efficiently allocating healthcare resources. Specifically, the use of Bayes' theorem for calculating disease probability, identifying risk groups through clustering algorithms, and predicting disease progression via dynamic forecasting approaches has been proven effective. Systematic analysis of the collected data creates opportunities to enhance prevention effectiveness and develop individualized treatment plans.
| Mualliflar | Mengturayev, Farxod, Nishanov, Axram, Allayarov, Uktamjon, Xaydarov, Sherali |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-03-23 |
| Son | 1 |
| Betlar | 187-194 |
| Til | Rus |
Endocrine diseases, prevention, early diagnosis, mathematical modeling, classification, clustering.
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