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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Early Intelligent Identification of Diabetic Complications

Early Intelligent Identification of Diabetic Complications

Guo, Shuli
Han, Lina
Guo, Yanan

Hardcover

Technology & EngineeringApplicationsGeneral Computers

PREORDER - Expected ship date October 28, 2026

ISBN10: 9819248833
ISBN13: 9789819248834
Publisher: Springer
Published: Oct 28 2026
Pages: 115
Language: English

This book delves into the AI practical application in the association patterns & prediction models of diabetes complications. In view of dataset preprocessing and clustering, a fusion clustering algorithm is proposed in combination with the self-organizing map neural network, and the superiority of the improved algorithm is proved through comparative experiments, overcoming the sensitive clustering centers and uncertain number of clustering categories of the traditional fuzzy c-means algorithm. For designing the association pattern of diabetic complications based on the improved Apriori algorithm, matrix compression parallelization is investigated to improve the mining efficiency of the Apriori algorithm. At the same time, the PS interest degree is improved by combining subjective factors and rule credibility, and the IPSR interest degree measurement is proposed to ensure the effectiveness of the mining results. Then, a diabetes complication prediction model is established based on the improved least squares support vector machine. The sparrow search algorithm is used to optimize the model parameters, the prediction deviation is corrected in combination with the deep belief network to further improve the prediction accuracy, and the particle swarm optimization algorithm is used to realize the adaptive selection of the network structure. At last, an intelligent early warning system using the Vue.js framework and the Flask framework for diabetes complications is designed, which realizes the following functions such as visualization of common diabetes' complication association patterns, prediction of diabetes complications, and query of diabetes patient information.

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