Method of Physiological State Abnormality Discrimination Based on Improved KNN Algorithm

Physiological state abnormality due to genetic diseases, excessive exercise, etc. is becoming a fatal killer endangering people's life and safety because of its hidden characteristics. K-Nearest Neighbor(KNN) Algorithm is widely used in various fields due to its simple implementation, but when the sample capacity is too large or the feature attributes are too many, the classification efficiency decreases significantly. This paper proposed an improved KNN(IKNN) algorithm based on clustering by hierarchically clustering the data in data pre-processing, which reduced the search space of the algorithm and effectively improved the search efficiency. When the improved KNN algorithm was applied in the physiological state abnormality discrimination field, which better improved the efficiency and accuracy of physiological abnormality discrimination. Results show that this could provide an effective guarantee for the early discovery of physiological parameter abnormality symptom, the timely adoption of dispositive measures, and the maintenance of people's life safety.