Feature selection and syndrome classification for rheumatoid arthritis patients with Traditional Chinese Medicine treatment

Predicting TCM patterns in PCOS patients: An exploration of feature selection methods and multi-label machine learning models

Background

Traditional Chinese Medicine (TCM) offers individualized treatment for Polycystic Ovary Syndrome (PCOS) through pattern differentiation, but the subjectivity of TCM diagnoses can lead to inconsistent outcomes. Integrating machine learning (ML) offers an objective basis to support TCM diagnoses. This study aims to evaluate various feature selection techniques and multi-label ML algorithms to develop an effective predictive model for classifying TCM patterns in PCOS patients, thereby enhancing diagnostic standardization and treatment personalization.

Methods

The study utilized a dataset comprising 432 patients with PCOS, exhibiting one or more of five TCM patterns. Feature selection began with Variance Thresholding (VT), followed by a comparison of five advanced techniques: Statistical Analysis Test, Recursive Feature Elimination with Cross-Validation (RFECV), Least Absolute Shrinkage and Selection Operator Regression, BorutaShap, and ReliefF. To ascertain the most effective model for predicting PCOS TCM patterns, four ML algorithms-Support Vector Machine, Logistic Regression, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks-were evaluated against the identified feature set.

Results

VT reduced the feature count from 224 to 174. RFECV emerged as the most effective feature selection method, identifying 67 key features. XGBoost emerged as the top-performing model, demonstrating superior testing accuracy (0.7870), F1 score (0.9519), and Hamming loss (0.0481) with RFECV-optimized features.

Conclusions

The RFECV-XGBoost model proved effective for classifying TCM patterns in PCOS. It emphasizes the necessity of precise feature selection and the significant capabilities of ML in advancing TCM pattern diagnostics, marking a significant step toward enhancing precise and personalized healthcare in biomedical studies.

A review of traditional Chinese medicine diagnosis using machine learning: Inspection, auscultation-olfaction, inquiry, and palpation

Traditional Chinese medicine (TCM) is an essential part of the Chinese medical system and is recognized by the World Health Organization as an important alternative medicine. As an important part of TCM, TCM diagnosis is a method to understand a patient's illness, analyze its state, and identify syndromes. In the long-term clinical diagnosis practice of TCM, four fundamental and effective diagnostic methods of inspection, auscultation-olfaction, inquiry, and palpation (IAOIP) have been formed. However, the diagnostic information in TCM is diverse, and the diagnostic process depends on doctors' experience, which is subject to a high-level subjectivity. At present, the research on the automated diagnosis of TCM based on machine learning is booming. Machine learning, which includes deep learning, is an essential part of artificial intelligence (AI), which provides new ideas for the objective and AI-related research of TCM. This paper aims to review and summarize the current research status of machine learning in TCM diagnosis. First, we review some key factors for the application of machine learning in TCM diagnosis, including data, data preprocessing, machine learning models, and evaluation metrics. Second, we review and summarize the research and applications of machine learning methods in TCM IAOIP and the synthesis of the four diagnostic methods. Finally, we discuss the challenges and research directions of using machine learning methods for TCM diagnosis.

Diagnosis knowledge constrained network based on first-order logic for syndrome differentiation

Traditional Chinese medicine (TCM) has been recognized worldwide as a valuable asset of human medicine. The procedure of TCM is to treatment based on syndrome differentiation. However, the effect of TCM syndrome differentiation relies heavily on the experience of doctors. The gratifying progress of machine learning research in recent years has brought new ideas for TCM syndrome differentiation. In this paper, we propose a deep network model for TCM syndrome differentiation, which improves network performance by injecting TCM syndrome differentiation knowledge in the form of first-order logic into the deep network. Experimental results show that the accuracy of our proposed model reaches 89%, which is significantly better than the deep learning model MLP and other traditional machine learning models. In addition, we present the collected and formatted TCM syndrome differentiation (TSD) dataset, which contains more than 40,000 TCM clinical records. Moreover, 45 symptoms (""), 322 patterns(""), and more than 500 symptoms are labeled in TSD respectively. To the best of our knowledge, this is the first TCM syndrome differentiation dataset labeling diseases, syndromes and pattern. Such detailed labeling is helpful to explore the relationship between various elements of syndrome differentiation.

Standardization, objectification, and essence research of traditional Chinese medicine syndrome: A 15-year bibliometric and content analysis from 2006 to 2020 in Web of Science database

The standardization, objectification, and essence research of traditional Chinese medicine (TCM) syndrome influence the modernization and international development of TCM syndrome. A total of 253 relevant publications collected from the Web of Science Core Collection database from 2006 to 2020 were analyzed by bibliometric and content methods. The co-occurrence analysis of countries, institutions, journals, authors, and keywords analysis were carried out by using Citespace software. The high-yield institutions and high-impact authors contributed to TCM syndrome publications were concentrated in China. Since 2012, driven by some groundbreaking publications, the number of TCM syndrome literatures has increased rapidly. According to the results of bibliometric and content analysis, research hotspots in TCM syndrome in the last 15 years can be summarized in six aspects: (a) objectification research of four TCM diagnostic methods, (b) omics technology for the essence research of TCM syndrome, (c) research on TCM syndrome evaluation scale, (d) metagenomic technology for the essence research of TCM syndrome, (e) data mining technology for TCM syndrome differentiation, and (f) systematic research on TCM syndromes of chronic hepatitis B. Emerging trends can be identified according to the most recent keywords bursts: (a) TCM syndrome diagnostic models with multiple indexes should be constructed to develop personalized medicine. (b) The connotation of TCM syndrome should be verified through "syndrome detecting from recipe used," and the screened potential markers of TCM syndrome need clinical verification. (c) The intervention and integration of multi-disciplines is expected to find a new breakthrough in the research of TCM syndrome.

Hierarchical Harris hawks optimizer for feature selection

INTRODUCTION

The main feature selection methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must include a swarm intelligence algorithm, and its performance in feature selection is closely related to the algorithm's quality. Therefore, it is essential to choose and design a suitable algorithm to improve the performance of the feature selection method based on the wrapper. Harris hawks optimization (HHO) is a superb optimization approach that has just been introduced. It has a high convergence rate and a powerful global search capability but it has an unsatisfactory optimization effect on high dimensional problems or complex problems. Therefore, we introduced a hierarchy to improve HHO's ability to deal with complex problems and feature selection.

OBJECTIVES

To make the algorithm obtain good accuracy with fewer features and run faster in feature selection, we improved HHO and named it EHHO. On 30 UCI datasets, the improved HHO (EHHO) can achieve very high classification accuracy with less running time and fewer features.

METHODS

We first conducted extensive experiments on 23 classical benchmark functions and compared EHHO with many state-of-the-art metaheuristic algorithms. Then we transform EHHO into binary EHHO (bEHHO) through the conversion function and verify the algorithm's ability in feature extraction on 30 UCI data sets.

RESULTS

Experiments on 23 benchmark functions show that EHHO has better convergence speed and minimum convergence than other peers. At the same time, compared with HHO, EHHO can significantly improve the weakness of HHO in dealing with complex functions. Moreover, on 30 datasets in the UCI repository, the performance of bEHHO is better than other comparative optimization algorithms.

CONCLUSION

Compared with the original bHHO, bEHHO can achieve excellent classification accuracy with fewer features and is also better than bHHO in running time.

Circumstantial Insights into the Potential of Traditional Chinese Medicinal Plants as a Therapeutic Approach in Rheumatoid Arthritis

The advanced era has invited a plethora of chronic and autoimmune infirmities unmistakably dominated by rheumatoid arthritis, occurring because of the equivocal causes, including ecological factors, genetic variations, etc. Unfortunately, it is winning pretty much in every stratum of the society in undefined age group of the population. Engineered drugs are accessible for the treatment; however, they do experience adverse effects as the treatment requires a prolonged duration worsened by noncompliance. To overwhelm it, certain pharmacological and molecular pathways are explored in the wake of Chinese herbs that prompted the prevention of this deteriorating autoimmune disease. The alcoholic extracts and decoctions are procured from Chinese herbs, such as Paeonia lactiflora, Glycyrrhiza uralensis, Tripterygium wilfordii, etc., which have been proved to manifest constructive pharmacological actions. The activities that were exhibited by extracts are significantly innocuous, non- toxic and potent to fix the affliction in contrast with the chemosynthetic drugs. Therefore, these Chinese herbs bring forth the potent anti-inflammatory, immune suppressing, anti-nociceptive, anti-neovascularizing, free radical scavenging activities and various other benefits to withstand several pathological events that usually endure the infirmity. It can be abridged that Chinese herbs possess assorted and selective therapeutic properties with profound safety and viability to treat this rheumatic disorder. Thus, this review aims to shed a light naturally originated treatment that is pertinent to provide invulnerable therapy exonerating from adverse effects, by restraining the occurrences of joint deformities, production of auto-antibodies, and inflammation.

Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans

Diabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin in pancreas that results in high glucose level in blood. The disease is also associated with other complications such as cardiovascular disease, retinopathy, neuropathy and nephropathy. The development of computer aided decision support system is inevitable field of research for disease diagnosis that will assist clinicians for the early prognosis of diabetes and to facilitate necessary treatment at the earliest. In this research study, a Traditional Chinese Medicine based diabetes diagnosis is presented based on analyzing the extracted features of panoramic tongue images such as color, texture, shape, tooth markings and fur. The feature extraction is done by Convolutional Neural Network (CNN)—ResNet 50 architecture, and the classification is performed by the proposed Deep Radial Basis Function Neural Network (RBFNN) algorithm based on auto encoder learning mechanism. The proposed model is simulated in MATLAB environment and evaluated with performance metrics—accuracy, precision, sensitivity, specificity, F1 score, error rate, and receiver operating characteristics (ROC). On comparing with existing models, the proposed CNN based Deep RBFNN machine learning classifier model outperformed with better classification performance and proving its effectiveness.

Syndrome Differentiation and Treatment Regularity in Traditional Chinese Medicine for Type 2 Diabetes: A Text Mining Analysis

Objective

The goal of this study was to systematically summarize and categorize the syndrome differentiation, medication rules, and acupoint therapy in the domestic traditional Chinese medicine (TCM) literature on type 2 diabetes mellitus (T2DM), such that guidelines and new insights can be provided for future practitioners and researchers.

Methods

Taking randomized controlled trials (RCTs) on the treatment of T2DM in TCM as the research theme, we searched for full-text literature in three major clinical databases, including CNKI, Wan Fang, and VIP, published between 1990 and 2020. We then conducted frequency statistics, cluster analysis, association rules extraction, and topic modeling based on a corpus of medical academic words extracted from 3,654 research articles.

Results

The TCM syndrome types, subjective symptoms, objective indicators, Chinese herbal medicine, acupuncture points, and TCM prescriptions for T2DM were compiled based on invigorating the kidney and Qi, nourishing Yin, and strengthening the spleen. Most TCM syndrome differentiation for T2DM was identified as "Zhongxiao" (the lesion in the spleen and stomach) and "Xiaxiao" (the lesion in the kidney) deficiency syndromes, and most medications and acupoint therapies were focused on the "Spleen Channel" and "Kidney Channel." However, stagnation of liver Qi was mentioned less when compared with other syndromes, which did not have symptomatic medicines.

Conclusion

This study provides an in-depth perspective for the TCM syndrome differentiation, medication rules, and acupoint therapy for T2DM and provides practitioners and researchers with valuable information about the current status and frontier trends of TCM research on T2DM in terms of both diagnosis and treatment.

Gender differences in Damp-Heat Syndrome: A review

Gender differences have important biological significance for medical research. In this study, a bias towards males was identified in animal experiments of Damp-Heat Syndrome in traditional Chinese medicine, as was first proposed by a data mining method. Combined with the correlation between Damp-Heat Syndrome in traditional Chinese medicine and Gender differences, it was considered that Gender-related factors have a significant influence on the development of Damp-Heat Syndrome in traditional Chinese medicine. However, most traditional Chinese medicine studies ignore the key significance of Gender-related factors. This study emphasises that the development of modern traditional Chinese medicine research needs to pay full attention to the biological significance of Gender-related factors and to apply this concept to the research on the Gender equivalence strategy in basic research and the practice of personalised medical diagnosis and clinical treatment.

Computational Traditional Chinese Medicine diagnosis: A literature survey

BACKGROUND AND OBJECTIVE

Traditional Chinese Medicine (TCM) diagnosis is based on the theoretical principles and knowledge, where it is steeped in thousands of years of history to diagnose various types of diseases and syndromes. It can be generally divided into four main diagnostic approaches: 1. Inspection, 2. Auscultation and olfaction, 3. Inquiry, and 4. Palpation, which are widely used in TCM hospitals in China and around the world. With the development of intelligent computing technology in recent years, computational TCM diagnosis has grown rapidly.

METHODS

In this paper, we aim to systematically summarize the development of computational TCM diagnosis based on four diagnostic approaches, mainly focusing on digital acquisition devices, collected datasets, and computational detection approaches (algorithms). Furthermore, all related works of this field are compared and explored in detail.

RESULTS

This survey provides the principles, applications, and current progress in computing for readers and researchers in terms of computational TCM diagnosis. Moreover, the future development direction, prospect, and technological trend of computational TCM diagnosis will also be discussed in this study.

CONCLUSIONS

Recent computational TCM diagnosis works are compared in detail to show the pros/cons, where we provide some meaningful suggestions and opinions on the future research approaches in this area. This work is useful for disease detection in computational TCM diagnosis as well as health management in the smart healthcare area.

INDEX TERMS

Computational diagnosis, Traditional Chinese Medicine, survey, smart healthcare.

Effects of laser acupuncture tele-therapy for rheumatoid arthritis elderly patients

Rheumatoid arthritis (RA) is a progressive common autoimmune disorder and is one of the most functional limiting diseases in elderly. Until recently, its treatment is mainly based on physical locations and meetings while being face to face. However, laser acupuncture tele-therapy approaches can significantly provide the patient with safety during the COVID-19 pandemic as well as changing the disorder’s prognosis. Sixty patients were assigned randomly into 2 groups with 1:1 ratio. Patients in group A are treated remotely by laser acupuncture in addition to methotrexate and a tele-rehabilitation program in the form of aerobic exercise training. Patients in group B are treated by methotrexate and a tele-rehabilitation program in the form of aerobic exercise. There was a statistically significant difference in health assessment questionnaire (HAQ) pre- and post-treatment in group A (p < 0.05). The C-reactive protein (CRP) and interleukin-6 (IL-6) inflammatory markers as well as the malondialdehyde (MDA) oxidative marker showed a significant reduction pre- and post-treatment in group A (p < 0.05). Additionally, there was a significant increase in the adenosine tri-phosphate (ATP) antioxidant marker pre- and post-treatment in group A (p < 0.05). The comparison between groups A and B showed a statistically significant post-treatment difference in RAQoL, CRP, IL-6, ATP, and MDA in group A than group B. Considering the significant improvement that was found in the laser acupuncture group, it can be concluded that the use of laser acupuncture as adjunctive was effective in the treatment of elderly patients with RA. ClinicalTrials.gov Identifier: NCT04758689