Towards the use of cybernetics for an enhanced cervical cancer care strategy  

作　　者：Ejay Nsugbe 

机构地区：[1]Nsugbe Research Labs,United Kingdom

出　　处：《Intelligent Medicine》2022年第3期117-126,共10页智慧医学（英文）

摘　　要：Background Cervical cancer is a prominent disease in women,with a high mortality rate worldwide.This cancer continues to be a challenge to concisely diagnose,especially in its early stages.The aim of this study was to pro-pose a unique cybernetic system which showcased the human-machine collaboration forming a superintelligence framework that ultimately allowed for greater clinical care strategies.Methods In this work,we applied machine learning(ML)models on 650 patients’data collected from Hospital Universitario de Caracas in Caracas,Venezuela,where ethical approval and informed consent were granted.The data were hosted at the University of California at Irvine(UCI)database for cancer prediction by using data purely from a patient questionnaire that include key cervical cancer drivers such as questions on sexually transmitted diseases and time since first intercourse in order to design a clinical prediction machine that can predict various stages of cervical cancer.Two contrasting methods are explored in the design of a ML-driven prediction machine in this study,namely,a probabilistic method using Gaussian mixture models(GMM),and fuzziness-based reasoning using the fuzzy c-means(FCM)clustering on the data from 650 patients.Results The models were validated using a K-Fold validation method,and the results show that both meth-ods could be feasibly deployed in a clinical setting,with the probabilistic method(produced accuracies of 80+%/classifier dependent)allowing for more detail in the grading of a potential cervical cancer prediction,albeit at the cost of greater computation power;the FCM approach(produced accuracies around 90+%/classifier dependent)allows for a more parsimonious modelling with a slightly reduced prediction depth in comparison.As part of the novelty of this work,a clinical cybernetic system is also proposed to host the prediction machine,which allows for a human-machine collaborative interaction and an enhanced decision support platform to aug-ment overall care strategies.Conclusion The presen

关 键 词：CYBERNETICS Artificial intelligence Cervical cancer Machine learning Public health 

分 类 号：R737.33[医药卫生—肿瘤]