USING THE DATA ANALYSIS KNN ALGORITHM TO MONITOR THE HEALTH OF AGRICULTURAL LANDOWNERS

Аннотация

This scientific article bstract This article investigates the application of the k-Nearest Neighbors (KNN) algorithm, a machine learning technique, for monitoring the health of agricultural landowners in Uzbekistan’s Fergana region. By leveraging data from wearable health devices, environmental sensors, and demographic surveys, the study evaluates KNN’s effectiveness in identifying health risks, including stress, fatigue, and pesticide exposure- related illnesses. Through a mixed-method approach, combining a literature review, statistical analysis, and a case study on cotton farmers, the research identifies key challenges such as data privacy concerns, limited access to wearable technologies, and insufficient technical training. Strategic recommendations include developing secure data platforms, optimizing KNN for resource-constrained environments, and implementing farmer training programs. Keywords: Health monitoring, k-Nearest Neighbors, data analysis, agricultural landowners, wearable devices, environmental sensors, machine learning, health risks, sustainable agriculture, farmer training.