Abstract: In this paper, by studying the application and analysis of KNN （k-nearest neighbor） algorithm in the field of disease prediction, two shortcomings of KNN are summarized, and the F_KNN （cyclic search nearest neighbor） algorithm is proposed: 1） for faults of KNN large amount of calculation and low efficiency, this paper uses the FLANN （quick nearest neighbor search） to loop search the nearest point of sample under test, record the number of nearest neighbor points as nearest neighbor ideas set, calculate using the sample subset to replace the complete treatment, can reduce the amount of calculation, greatly improve the efficiency of the KNN algorithm; 2） In view of the shortcoming of KNN that it is difficult to classify high-dimensional data sets, AHP （analytic hierarchy process） is adopted in this paper to study the correlation of characteristic attributes of samples, and appropriate parameters are used to assign weights, which improves the accuracy of KNN algorithm. In this paper, a set of cerebral apoplexy data sets are used to test the optimized algorithm, and the experimental results show that the accuracy of F_KNN is 96.2%. Compared with the traditional KNN, it improves the classification performance and greatly improves the efficiency of the algorithm. When dealing with high dimensional and large data sets, F_KNN algorithm has obvious advantages and has a good application prospect.

Key words: KNN, F_KNN, FLANN, AHP, stroke, disease prediction