A comparative study of machine learning classifiers for risk prediction of asthma disease

Artificial intelligence applications in allergic rhinitis diagnosis: Focus on ensemble learning

Background

The diagnosis of allergic rhinitis (AR) primarily relies on symptoms and laboratory examinations. Due to limitations in outpatient settings, certain tests such as nasal provocation tests and nasal secretion smear examinations are not routinely conducted. Although there are clear diagnostic criteria, an accurate diagnosis still requires the expertise of an experienced doctor, considering the patient's medical history and conducting examinations. However, differences in physician knowledge and limitations of examination methods can result in variations in diagnosis.

Objective

Artificial intelligence is a significant outcome of the rapid advancement in computer technology today. This study aims to present an intelligent diagnosis and detection method based on ensemble learning for AR.

Method

We conducted a study on AR cases and 7 other diseases exhibiting similar symptoms, including rhinosinusitis, chronic rhinitis, upper respiratory tract infection, etc. Clinical data, encompassing medical history, clinical symptoms, allergen detection, and imaging, was collected. To develop an effective classifier, multiple models were employed to train on the same batch of data. By utilizing ensemble learning algorithms, we obtained the final ensemble classifier known as adaptive random forest-out of bag-easy ensemble (ARF-OOBEE). In order to perform comparative experiments, we selected 5 commonly used machine learning classification algorithms: Naive Bayes, support vector machine, logistic regression, multilayer perceptron, deep forest (GC Forest), and extreme gradient boosting (XGBoost).To evaluate the prediction performance of AR samples, various parameters such as precision, sensitivity, specificity, G-mean, F1-score, and area under the curve (AUC) of the receiver operating characteristic curve were jointly employed as evaluation indicators.

Results

We compared 7 classification models, including probability models, tree models, linear models, ensemble models, and neural network models. The ensemble classification algorithms, namely ARF-OOBEE and GC Forest, outperformed the other algorithms in terms of the comprehensive classification evaluation index. The accuracy of G-mean and AUC parameters improved by nearly 2% when compared to the other algorithms. Moreover, these ensemble classifiers exhibited excellent performance in handling large-scale data and unbalanced samples.

Conclusion

The ARF-OOBEE ensemble learning model demonstrates strong generalization performance and comprehensive classification abilities, making it suitable for effective application in auxiliary AR diagnosis.

Image-free recognition of moderate ROP from mild with machine learning algorithm on plasma Raman spectrum

The retinopathy of prematurity (ROP) can cause serious clinical consequences and, fortunately, it is remediable while the time window for treatment is relatively narrow. Therefore, it is urgent to screen all premature infants and diagnose ROP degree timely, which has become a large workload for pediatric ophthalmologists. We developed a retinal image-free procedure using small amount of blood samples based on the plasma Raman spectrum with the machine learning model to automatically classify ROP cases before medical intervention was performed. Statistical differences in infrared Raman spectra of plasma samples were found among the control, mild (ZIIIS1), moderate (ZIIIS2 & ZIIS1), and advanced (ZIIS2) ROP groups. With the different wave points of Raman spectra as the inputs, the outputs of our support vector machine showed that the area under the curves in the receiver operating characteristic (AUC) were 0.763 for the pair comparisons of the control with the mild groups, 0.821 between moderate and advanced groups (ZIIS2), while more than 90% in comparisons of the other four pairs: control vs. moderate (0.981), control vs. advanced (0.963), mild vs. moderate (0.936), and mild vs. advanced (0.953), respectively. Our study could advance principally the ROP diagnosis in two dimensions: the moderate ROPs have been classified remarkably from the mild ones, which leaves more time for the medical treatments, and the procedure of Raman spectrum with a machine learning model based on blood samples can be conveniently promoted to those hospitals lacking of the pediatric ophthalmologists with experience in reading retinal images.

Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases

INTRODUCTION

Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment.

AREAS COVERED

This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation.

EXPERT OPINION

Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.

Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022)

BACKGROUND AND OBJECTIVE

Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality.

METHODS

We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance.

RESULTS

We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent.

CONCLUSIONS

Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.

Supervised Machine Learning Methods for Seasonal Influenza Diagnosis

Influenza has been a stationary disease in Mexico since 2009, and this causes a high cost for the national public health system, including its detection using RT-qPCR tests, treatments, and absenteeism in the workplace. Despite influenza's relevance, the main clinical features to detect the disease defined by international institutions like the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC) do not follow the same pattern in all populations. The aim of this work is to find a machine learning method to facilitate decision making in the clinical differentiation between positive and negative influenza patients, based on their symptoms and demographic features. The research sample consisted of 15480 records, including clinical and demographic data of patients with a positive/negative RT-qPCR influenza tests, from 2010 to 2020 in the public healthcare institutions of Mexico City. The performance of the methods for classifying influenza cases were evaluated with indices like accuracy, specificity, sensitivity, precision, the f1-measure and the area under the curve (AUC). Results indicate that random forest and bagging classifiers were the best supervised methods; they showed promise in supporting clinical diagnosis, especially in places where performing molecular tests might be challenging or not feasible.

Machine Learning Enhanced Optical Spectroscopy for Disease Detection

Optical spectroscopy plays an important role in disease detection. Improving the sensitivity and specificity of spectral detection has great importance in the development of accurate diagnosis. The development of artificial intelligence technology provides a great opportunity to improve the detection accuracy through machine learning methods. In this Perspective, we focus on the combination of machine learning methods with the optical spectroscopy methods widely used for disease detection, including absorbance, fluorescence, scattering, FTIR, terahertz, etc. By comparing the spectral analysis with different machine learning methods, we illustrate that the support vector machine and convolutional neural network are most effective, which have potential to further improve the classification accuracy to distinguish disease subtypes if these machine learning methods are used. This Perspective broadens the scope of optical spectroscopy enhanced by machine learning and will be useful for the development of disease detection.

The Significance of Software Engineering to Forecast the Public Health Issues: A Case of Saudi Arabia

In the recent years, public health has become a core issue addressed by researchers. However, because of our limited knowledge, studies mainly focus on the causes of public health issues. On the contrary, this study provides forecasts of public health issues using software engineering techniques and determinants of public health. Our empirical findings show significant impacts of carbon emission and health expenditure on public health. The results confirm that support vector machine (SVM) outperforms the forecasting of public health when compared to multiple linear regression (MLR) and artificial neural network (ANN) technique. The findings are valuable to policymakers in forecasting public health issues and taking preemptive actions to address the relevant health concerns.

A hybrid cost-sensitive machine learning approach for the classification of intelligent disease diagnosis

Automatic risk classification of diseases is one of the most significant health problems in medical and healthcare domain. However, the related studies are relative scarce. In this paper, we design an intelligent diagnosis model based on optimal machine learning algorithms with rich clinical data. First, the disease risk classification problem based on machine learning is defined. Then, the K-means clustering algorithm is used to validate the class label of given data, thereby removing misclassified instances from the original dataset. Furthermore, naive Bayesian algorithm is applied to build the final classifier by using 10-fold cross-validation method. In addition, a novel class-specific attribute weighted approach is adopted to alleviate the conditional independence assumption of naive Bayes, which means we assign each disease attribute a specific weight for each class. Last but not least, a hybrid cost-sensitive disease risk classification model is formulated, and a practical example from the University of California Irvine (UCI) machine learning database is used to illustrate the potential of the proposed method. Experimental results demonstrate that the approach is competitive with the state-of-the-art classifiers.

Optimised Pre-Processing of Raman Spectra for Colorectal Cancer Detection Using High-Performance Computing

Spectral pre-processing is an essential step in data analysis for biomedical diagnostic applications of Raman spectroscopy, allowing the removal of undesirable spectral contributions that could mask biological information used for diagnosis. However, due to the specificity of pre-processing for a given sample type and the vast number of potential pre-processing combinations, optimisation of pre-processing via a manual "trial and error" format is often time intensive with no guarantee that the chosen method is optimal for the sample type. Here we present the use of high-performance computing (HPC) to trial over 2.4 million pre-processing permutations to demonstrate the optimisation on the pre-processing of human serum Raman spectra for colorectal cancer detection. The effect of varying pre-processing order, using extended multiplicative scatter correction, spectral smoothing, baseline correction, binning and normalization was considered. Permutations were assessed on their ability to detect patients with disease using a random forest (RF) algorithm trained with 102 patients (510 spectra) and independently tested with a set of 439 patients (1317 spectra) in a primary care patient cohort. Optimising via HPC enables improved performance in diagnostic abilities, with sensitivity increasing by 14.6%, specificity increasing by 6.9%, positive predictive value increasing by 3.4%, and negative predictive value increasing by 2.4% when compared to a standard pre-processing optimisation. Ultimate values of these metrics are very important for diagnostic adoption, and once diagnostics demonstrate good accuracy these types of optimisations can make a significant difference to roll-out of a test and demonstrating advantages over existing tests. We also provide tips/recommendations for pre-processing optimisation without the use of HPC. From the HPC permutations, recommendations for appropriate parameter constraints for conducting a more basic pre-processing optimisation are also detailed, thus helping model development for researchers not having access to HPC.

Rapid identification of papillary thyroid carcinoma and papillary microcarcinoma based on serum Raman spectroscopy combined with machine learning models

Thyroid carcinoma is one kind of cancer with the highest diagnosis rate in the endocrine system, and its main histological subtype is papillary thyroid carcinoma (PTC) accounting for 80% of thyroid malignancies. In recent years, the incidence of thyroid cancer has increased exponentially, and its substantial increase was closely related to the overdiagnosis of papillary microcarcinoma (PMC). Therefore, early and accurate identification of PTC and PMC can prevent patients from being irreversibly damaged. This study aimed to identify PTC and PMC using Raman spectroscopy. We collected serum Raman spectra from 16 patients with PTC and 31 patients with PMC. Firstly, the collected imbalance data were preprocessed using the synthetic minority over-sampling technique (SMOTE). Then, the equalized data were dimensionality reduced by principal component analysis (PCA). Finally, the processed data were fed into the single decision tree (DT) classifier, as well as the random forest (RF) built on the idea of Boosting ensemble and the Adaptive Boosting (Adaboost) model built on the idea of Bagging ensemble for classification. The classification accuracy of the three models in the testing set were 75.38%, 81.54%, and 84.61%, respectively. Compared with the DT classifier, the accuracy of the models introducing the idea of ensemble learning was enhanced by 6.16% and 9.23%, respectively. The best model was the Adaboost. This result demonstrates that serum Raman spectroscopy combined with an ensemble learning algorithm was feasible in rapidly identifying PTC and PMC. At the same time, the method has great potential for application in the field of clinical diagnosis.

Application of serum mid-infrared spectroscopy combined with an ensemble learning method in rapid diagnosis of gliomas

The diffuse growth of glioma cells leads to gliomatosis, which has less cure rate and high mortality. As the severity deepens, the treatment difficulty and mortality of glioma patients gradually increase. Therefore, a rapid and non-invasive diagnostic technique is very important for glioma patients. The target of this study is to classify contract subjects and glioma patients by serum mid-infrared spectroscopy combined with an ensemble learning method. The spectra were normalized and smoothed, and principal component analysis (PCA) was utilized for dimensionality reduction. Particle swarm optimization-support vector machine (PSO-SVM), decision tree (DT), logistic regression (LR) as well as random forest (RF) were used as base classifiers, and AdaBoost integrated learning was introduced. AdaBoost-SVM, AdaBoost-LR, AdaBoost-RF and AdaBoost-DT models were established to discriminate glioma patients. The single classification accuracy of the four models for the test set was 87.14%, 90.00%, 92.00% and 90.86%, respectively. For the purpose of further improving the prediction accuracy, the four models were fused at decision level, and the final classification accuracy of the test set reached 94.29%. Experiments show that serum infrared spectroscopy combined with the ensemble learning method algorithm shows wonderful potential in non-invasive, fast and precise identification of glioma patients, and can also be used for reference in intelligent diagnosis of other diseases.

Seeing the Forest for the Trees: Evaluating Population Data in Allergy-Immunology

A population-level study is essential for understanding treatment effects, epidemiologic phenomena, and health care best practices. Evaluating large populations and associated data requires an analytic framework, which is commonly used by statisticians, epidemiologists, and data scientists. This document will serve to provide an overview of these commonly employed methods in allergy and immunology research. We will draw upon recent examples from the allergy-immunology literature to contextualize discrete principles of relevance to population-level analysis that include statistical features of a study population, elements of statistical inference, regression analysis, and an overview of machine learning practices. Our intent is to guide the reader through a practical description of this important quantitative discipline and facilitate greater understanding about data and result display in the medical literature.

Intelligent spectral algorithm for pigments visualization, classification and identification based on Raman spectra

Raman spectroscopy is a molecular vibrational spectroscopic technique has developed rapidly in recent years, especially in rapid field detection. In this paper, we discuss the Raman spectral pretreatment method and classification algorithm by using nearly 300 pigments spectral data as an example. Here, more than 5 kinds of classification algorithms such as SVM, KNN, ANN and et al are used to sovle the problem of pigments visualization, classification and identification via Raman spectral, and the results show that most of the algorithms fit well, with an accuracy of 90%. Moreover, SNR (Signal to noise ratio) is introduced to evaluate the stability of our algorithm. When the SNR is low, the accuracy of the algorithm decreases sharply. When the SNR was 1, the accuracy rate reached the highest value of 39.46%. In order to slove this problem, the flattopwin, hanning, blackman algorithm was introduced to denoise the signal with low SNR, even when SNR = 1, the signal is 80% accurate. It is proved that in the extreme case of this application, the algorithm still maintains good accuracy, and our research pave the way to use interlligent algorithms to solve the problems in the fields of Raman spectral detection.

Machine learning in asthma research: moving toward a more integrated approach

Introduction: Big data are reshaping the future of medicine. The growing availability and increasing complexity of data have favored the adoption of modern analytical and computational methodologies in every area of medicine. Over the past decades, asthma research has been characterized by a shift in the way studies are conducted and data are analyzed. Motivated by the assumptions that 'data will speak for themselves', hypothesis-driven approaches have been replaced by data-driven hypotheses-generating methods to explore hidden patterns and underlying mechanisms. However, even with all the advancement in technologies and the new important insight that we gained to understand and characterize asthma heterogeneity, very few research findings have been translated into clinically actionable solutions.Areas covered: To investigate some of the fundamental analytical approaches adopted in the current literature and appraise their impact and usefulness in medicine, we conducted a bibliometric analysis of big data analytics in asthma research in the past 50 years.Expert opinion: No single data source or methodology can uncover the complexity of human health and disease. To fully capitalize on the potential of 'big data', we will have to embrace the collaborative science and encourage the creation of integrated cross-disciplinary teams brought together around technological advances.

Multi disease-prediction framework using hybrid deep learning: an optimal prediction model

Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient's symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to "Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson's disease, and Alzheimer's disease", from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like "Deep Belief Network (DBN) and Recurrent Neural Network (RNN)". As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

Comparison Analysis of Prediction Model for Respiratory Diseases

Millions of people around the world have one or many respiratory-related illnesses. Many chronic respiratory diseases like asthma, COPD, pneumonia, respiratory distress, etc. are considered to be a significant public health burden. To reduce the mortality rate, it is better to perform early prediction of respiratory disorders and treat them accordingly. To build an efficient prediction model for various types of respiratory diseases, machine learning approaches are used. The proposed methodology builds classifier model using supervised learning algorithms like random forest, decision tree, and multi-layer perceptron neural network (MLP-NN) for the detection of different respiratory diseases of ICU admitted patients. It achieves accuracy of nearly 99% by various machine learning approaches.

Diagnostic significance of C-reactive protein and hematological parameters in acute toxoplasmosis

Abstract

Toxoplasmosis is a zoonosis caused by Toxoplasma gondii, which can be acquired by oral contact and may cause severe health problems especially for pregnant (congenital toxoplasmosis) and immunocompromised patients. This study aimed to verify the diagnostic significance of hematological parameters and C-reactive protein (CRP) for toxoplasmosis acute detection. A case-control study was carried out between December 2017 and May 2018, in samples of convenience independent of age and sex. The case group was formed by 25 patients with positive anti-Toxoplasma gondii IgG/IgM antibody and the control group was formed by 21 patients with non-positive anti-Toxoplasma gondii IgG/IgM antibody. The results of the hematological parameters and CRP were analyzed in these patients. The patients with Toxoplasma gondii IgM antibody reagent showed higher lymphocytes counting and lower neutrophils counting than the control group. C-reactive protein levels were not different between the groups case and control. ROC curve analysis highlighted that the cut-off value of > 32.00% for lymphocytes and < 57.50% for neutrophils were able to produce specificity higher than 90% for IgM antibody detection. The Naïve Bayes classifier was considered suitable (AUC ≈ 0.700) to separate both groups according to their white cell counting. Changes in lymphocytes and neutrophils may be useful parameters for toxoplasmosis identification and may be used as a tool in the complementary diagnosis of toxoplasmosis.

Graphic abstract