Research On The Application Of Machine Learning In The Classification Of Myoptosis

As the global aging trend intensifies,various geriatric diseases have gradually become the focus of social attention.Sarcopenia is a common geriatric disease,and its incidence is increasing year by year,seriously affecting the quality of life of the elderly,and gradually becoming an important public health problem.With the deepening of research on sarcopenia,people pay more and more attention to the early screening and early diagnosis of sarcopenia.Early diagnosis and intervention of sarcopenia can effectively prevent sarcopenia from developing into advanced and severe stages,thereby reducing the suffering of the elderly and the burden on families and society.At present,the diagnosis of sarcopenia mainly relies on physical examination and imaging examination,but this method requires professional doctors,is expensive,complicated to operate,and it is difficult to implement universal work in grassroots medical institutions such as communities.Therefore,the development of a high-efficiency,low-cost,and easy-to-implement early screening method for sarcopenia has important practical significance and application prospects.This study aims to explore the importance of screening and early diagnosis of sarcopenia in order to improve the prevention and treatment of the disease.Sarcopenia is a common neuromuscular disease,early detection and intervention are very important for the recovery and survival of patients.Therefore,this study aims to establish a reliable classification model for sarcopenia,which can accurately diagnose three states: normal,pre sarcopenia,and sarcopenia.The data of this study comes from the clinical data of the hospital,which contains 95 indicators,covering 7 aspects of demographics,exercise,strength,obesity,body,physiology and biochemistry,and underlying diseases.The dependent variable is the prevalence of sarcopenia,which is a three-category value.In terms of data processing,this study uses random forest to fill in missing values,and performs one-hot encoding for categorical variables,and standardizes continuous variables.In addition,in medical classification research,due to the small number of patients compared with the normal population,the samples are often unbalanced.If the conventional machine learning algorithm is directly used for classification,the classifier will often be too biased towards normal samples and cannot be accurately classified.Identify minority class samples.Therefore,solving the problem of sample imbalance is of great significance for fully mining the information in minority samples.This study uses the SMOTE method to balance the distribution of categories by randomly filling the minority categories to avoid inaccurate model predictions.In terms of model selection,this paper chooses four machine learning models,including support vector machine(SVM),random forest(RF),Adaboost and KNN.In terms of modeling ideas,firstly,feature selection is performed through random forest,and a total of 32 features above the average level of importance are selected,and then these32 features are put into the model for training,and finally by comparing the classification accuracy,precision rate,Recall,F1-score,number of iterations of model accuracy,and average score of 5-fold cross-validation.The results of the study showed that the random forest classification model performed best,with high accuracy,high precision and high recall.This shows that the model has high accuracy and reliability in classifying sarcopenia.In summary,this study established a reliable sarcopenia classification model,which can accurately diagnose the disease state of patients,provides an effective tool for the screening and early diagnosis of sarcopenia,and helps to improve the patient’s Recovery and survival rate,has important clinical potential application value.