Application of Machine Learning Algorithms in Early Detection of Alzheimer's Disease
Altsgeymer Kasalligini Erta Aniqlashda Mashinali O'rgatish Algoritmlarini Qo'llanilishi
Abstract
This paper investigates the application of machine learning algorithms for the early detection of Alzheimer's disease. The study evaluates multiple classification algorithms including support vector machines, random forests, and deep neural networks using neuroimaging and clinical datasets. Feature selection and dimensionality reduction techniques are applied to improve model performance and interpretability. The results demonstrate that machine learning approaches can achieve high diagnostic accuracy in identifying early-stage Alzheimer's disease, offering a promising avenue for non-invasive and cost-effective screening tools in clinical practice.
Abstract (Secondary Language)
Ushbu maqolada Altsgeymer kasalligini erta aniqlashda mashina o'rganish algoritmlarining qo'llanilishi o'rganiladi. Neyrovizualizatsiya va klinik ma'lumotlar to'plamlaridan foydalangan holda support vektor mashinalari, tasodifiy o'rmonlar va chuqur neyron tarmoqlar kabi bir nechta klassifikatsiya algoritmlari baholanadi. Natijalar shuni ko'rsatadiki, mashina o'rganish yondashuvlari Altsgeymer kasalligining dastlabki bosqichlarini aniqlashda yuqori diagnostik aniqlikka erishishi mumkin.