Research On Dynamic Model Of Breast Cancer Diagnosis And Treatment Based On Machine Learning

Breast cancer is considered to be the most common cancer in the world and the main cause of cancer deaths.At present,there is no systemic method for early non-invasive differential diagnosis of breast cancer.This research will provide a new diagnosis method and treatment strategy for the early detection of breast cancer in clinical practice and the realization of precise treatment of breast cancer.Through machine learning,system dynamics and other computational modeling methods,find the correlation between each data information and the diagnosis and treatment of breast cancer.Judge the occurrence and development of breast cancer,and apply it to clinical diagnosis and treatment tasks such as early screening,early diagnosis,nature judgment,surgical decision-making,and prognosis evaluation of breast cancer.This article first discretizes the ultrasound detection data and pathological prognosis data,and then uses multiple interpolation methods to interpolate the missing values.Based on the traditional CBA algorithm,an improved weighted association rule classification algorithm is proposed.This algorithm is improved in the rule generation and rule pruning stages,and feature weights based on the combination of expert knowledge and feature selection are introduced.And use the statistical harmonic mean HM method to reconcile the weighted support and confidence of the generated frequent rules.Three methods of information gain,variance method and pearson coefficient are used for feature selection.Finally,the improved algorithm is applied to ultrasound detection data and pathological prognosis data,and compared with the four algorithms of CBA,C4.5,CMAR,WCBA in terms of accuracy,specificity,sensitivity,AUC,etc.The experimental results show that the improved classification algorithm of weighted association rules has better classification effect.The association rules obtained by the association classification algorithm are added as a driving mechanism to the breast cancer diagnosis dynamics model to dynamically evolve the breast cancer diagnosis process.On the basis of breast cancer diagnosis model,the treatment mode and pathological indicators were introduced to construct a breast cancer treatment kinetic model,and the relationship between various treatment methods,genes and other related pathological indicators and five-year survival was analyzed.The simulation experiment results show that the diagnostic accuracy rate is 95.43%,and the five-year survival prediction accuracy rate is 90.57%.This result shows the rationality and accuracy of the breast cancer diagnostic kinetic model proposed in this study,which can provide a certain reference for the diagnosis of other diseases.Through the sensitivity analysis experiments on tumor size,axillary lymph node size,molecular classification,Stage,postoperative chemotherapy,and Ki67.The results show that tumor size and axillary lymph node size have certain auxiliary reference value for the diagnosis of breast cancer.The larger the size,the greater the possibility of serious disease,and the higher the BI-RADS level of diagnosis.Molecular classification,Ki67 and Stage are closely related to the five-year prognosis effect.The five-year prognosis effect of triple-negative patients is poor,and the high expression of Ki67 can obtain a good five-year prognosis effect.The above methods can provide scientific basis for the diagnosis and treatment of cancer,and assist doctors in making scientific decisions.