Abstract
Most developing countries face huge challenges in the medical field; scarce medical resources and inadequate medical personnel will affect the development and stability of the society. Therefore, for most developing countries, the development of intelligent medical systems can greatly alleviate the social contradictions arising from this problem. In this study, a new data decision-making intelligent system for prostate cancer based on perceptron neural network is proposed, which mainly makes decisions by associating some relevant disease indicators and combining them with medical images. Through data collection, analysis and integration of medical data, as well as the disease detection and decision-making process, patients are given an auxiliary diagnosis and treatment, so as to solve the problems and social contradictions faced by most developing countries. Through the study of hospitalization information of more than 8,000 prostate patients in three hospitals, about 2,156,528 data items were collected and compiled for experiment purposes. Experimental data shows that when the patient base increases from 200 to 8,000, the accuracy of the machine-assisted diagnostic system will increase from 61% to 87%, and the doctor's diagnosis rate will be reduced to 81%. From the study, it is concluded that when the patient base reaches a certain number, the diagnostic accuracy of the machine-assisted diagnosis system will exceed the doctor's expertise. Therefore, intelligent systems can help doctors and medical experts treat patients more effectively.
Highlights
Prostate cancer (PCA) is a human disease that occurs in malignant tumors of the prostate epithelium [1,2,3,4,5,6]
A machine-aided diagnosis system for prostate cancer based on perceptron neural network and big data was proposed to solve the problem of scarce medical resources caused by the large population and underdeveloped medical level in developing countries
The intelligent system takes the combination of six disease indicators and two medical image indicators as the input and continuously adjusts the weight and deviation of the neural network in the context of medical big data to form an intelligent diagnosis model based on the neural network
Summary
Prostate cancer (PCA) is a human disease that occurs in malignant tumors of the prostate epithelium [1,2,3,4,5,6]. In Europe and the United States, the incidence of male cancer patients ranked first, [7, 8] while the death rate ranked second. In America, the incidence of prostate cancer has risen to become the first place; the mortality rate is second only to lung cancer. Prostate cancer has become one of the most common cancers in the world and one of the most common malignancies among men in Europe and the United States [9]. In developing countries such as [10] China, the incidence rate is lower than that in many European and American countries. Due to the large population base in developing countries, the number of cases is not to be underestimated [11]
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