Classification of Acupuncture Points Based on the Bert Model*

In this paper, we explore the multi-classification problem of acupuncture acupoints based on Bert model, i.e., we try to recommend the best main acupuncture point for treating the disease by classifying and predicting the main acupuncture point for the disease, and further explore its acupuncture point grouping to provide the medical practitioner with the optimal solution for treating the disease and improving the clinical decision-making ability. The Bert-Chinese-Acupoint model was constructed by retraining on the basis of the Bert model, and the semantic features in terms of acupuncture points were added to the acupuncture point corpus in the fine-tuning process to increase the semantic features in terms of acupuncture points, and compared with the machine learning method. The results show that the Bert-Chinese Acupoint model proposed in this paper has a 3% improvement in accuracy compared to the best performing model in the machine learning approach.

Analysis of the Resolution of Crime Using Predictive Modeling

There has been evidence of crime in the US since colonization. In this article, we analyze the crime statistics of San Francisco and its resolution of crime recorded from January to September of the year 2018. We define resolution of crime as a target variable and study its relationship with other variables. We make several classification models to predict resolution of crime using several data mining techniques and suggest the best model for predicting resolution.

Argumentative Comparative Analysis of Machine Learning on Coronary Artery Disease

Cardiovascular disease (CVD) is a leading cause of death across the globe. Approximately 17.9 million of people die globally each year due to CVD, which comprises 31% of all death. Coronary Artery Disease (CAD) is a common type of CVD and is considered fatal. Predictive models that use machine learning algorithms may assist health workers in timely detection of CAD which ultimately reduces the mortality. The main purpose of this study is to build a predictive model that provides doctors and health care providers with personalized information to implement better and more personalized treatments for their patients. In this study, we use the publicly available Z-Alizadeh Sani dataset which contains random samples of 216 cases with CAD and 87 normal controls with 56 different features. The binary variable “Cath” which represents case-control status, is used the target variable. We study its relationship with other predictors and develop classification models using the five different supervised classification machine learning algorithms: Logistic Regression (LR), Classification Tree with Bagging (Bagging CART), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). These five classification models are used to investigate the detection of CAD. Finally, the performance of the machine learning algorithms is compared, and the best model is selected. Our results indicate that the SVM model is able to predict the presence of CAD more effectively and accurately than other models with an accuracy of 0.8947, sensitivity of 0.9434, specificity of 0.7826, and AUC of 0.8868.