Abstract
With the development of medical informatization, the data related to medical field are growing at an amazing speed, and medical big data appears. The mining and analysis of these data plays an important role in the prediction, monitoring, diagnosis, and treatment of tumor diseases. Therefore, this paper proposes a clustering algorithm of the high-order simulated annealing neural network algorithm and uses this algorithm to extract tumor disease-related big data, constructs training set according to the relevant information mined, designs a kind of dimension reduction model, aiming at the problem of excessive and wrong diagnosis and treatment in the diagnosis and treatment module of tumor disease monitoring mode, and establishes the corresponding control mechanism, so as to optimize the tumor disease monitoring mode. The results show that the clustering accuracy of the high-order simulated annealing neural network algorithm on different data sets (iris, wine, and Pima India diabetes) is 97.33%, 82.11%, and 70.56% and the execution time is 0.75 s, 0.562 s, and 1.092 s, which are better than those of the fast k-medoids algorithm and improved k-medoids clustering algorithm. To sum up, the high-order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining. The establishment of model M1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode.
Highlights
With the rapid development of medical informatization, medical data are growing rapidly, and the era of medical big data is coming
E chronic disease and nutrition monitoring system is mainly responsible for monitoring the patients with chronic diseases and related factors, the trend of nutritional diseases, and so on. e cause of death detection system is mainly responsible for studying the death level of population and plays an important role in optimizing the allocation of resources and evaluating the health of residents; the cancer registration system is mainly responsible for collecting cancer incidence and survival status information of residents so as to show the epidemic
On the wine data set, the accuracy of data clustering from high to low is high-order simulated annealing combined with K-medoid algorithm, hybrid neural network algorithm, k-medoids clustering algorithm based on improved granular computing, fast kmedoids algorithm, and PAM algorithm. e corresponding clustering accuracy is 82.11%, 70.79%, 70.79%, and 52.87%, respectively. e clustering algorithm with the highest clustering accuracy on Pima India diabetes data set is the high-order simulated annealing combined with the Kmedoid algorithm hybrid neural network algorithm proposed in this paper
Summary
Received 2 August 2021; Revised 25 August 2021; Accepted 1 September 2021; Published 19 October 2021. Erefore, this paper proposes a clustering algorithm of the high-order simulated annealing neural network algorithm and uses this algorithm to extract tumor disease-related big data, constructs training set according to the relevant information mined, designs a kind of dimension reduction model, aiming at the problem of excessive and wrong diagnosis and treatment in the diagnosis and treatment module of tumor disease monitoring mode, and establishes the corresponding control mechanism, so as to optimize the tumor disease monitoring mode. The high-order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining. E establishment of model M1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode The high-order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining. e establishment of model M1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
