Linear Regression Classification Research Articles

Contribution to Characterizing the Meat Quality of Protected Designation of Origin Serrana and Preta de Montesinho Kids Using the Near-Infrared Reflectance Methodology.

The aims of this study were to describe and compare the meat quality characteristics of male and female kids from the "Serrana" and "Preta de Montesinho" breeds certified as "Cabrito Transmontano" and reinforce the performance of near-infrared reflectance (NIR) spectra in predicting these quality characteristics and discriminating among breeds. Samples of Longissimus thoracis (n = 32; sixteen per breed; eight males and eight females) were used. Breed significantly affected meat quality characteristics, with only color and fatty acid (FA) (C12:0) being influenced by sex. The meat of the "Serrana" breed proved to be more tender than that of the "Preta de Montesinho". However, the meat from the "Preta de Montesinho" breed showed higher intramuscular fat content and was lighter than that from the "Serrana" breed, which favors its quality of color and juiciness. The use of NIR with the linear support vector machine regression (SVMR) classification model demonstrated its capability to quantify meat quality characteristics such as pH, CIELab color, protein, moisture, ash, fat, texture, water-holding capacity, and lipid profile. Discriminant analysis was performed by dividing the sample spectra into calibration sets (75 percent) and prediction sets (25 percent) and applying the Kennard-Stone algorithm to the spectra. This resulted in 100% correct classifications with the training data and 96.7% accuracy with the test data. The test data showed acceptable estimation models with R2 > 0.99.

ML Model for Stock Classification

Abstract: This study examines how effectively volatility indices can forecast stock market movements by applying different machine learning techniques. It uses a dataset from Kaggle for the TCS Company, including variables such as 'Date,' 'Symbol,' 'Series,' 'Prev Close,' 'Open,' 'High,' 'Low,' 'Last,' 'Close,' 'VWAP,' 'Volume,' 'Turnover,' 'Deliverable Volume,' and '%Deliverable.' Various models were tested and evaluated based on metrics like the AUC-ROC Curve, Accuracy, Precision, Recall, F1-Score, Cross-Validation Accuracy, and the Confusion Matrix. The findings reveal that the Linear Regression (Classification model) surpasses other models in forecasting stock market directions with the highest levels of accuracy across all evaluation metrics. This highlights the potential of machine learning methods in leveraging volatility indices to accurately predict stock market trends

Feature Importance Analysis and Machine Learning for Alzheimer’s Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio

Introduction: Alzheimer’s disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15–20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. Methods: To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results: The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs’ superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single feature category. Conclusion: Our study emphasizes the pivotal role of GLCM texture features in early Alzheimer’s detection.

Stock Price Prediction and Stock Behaviour using GA and LR with NIFTY50 index Based on Daily Charts

The price of a company's stock, which can increase in lockstep with the price of a single share, is the one of the indicator to measure its performance. Clients or stockholding companies find it challenging to make long-term projections regarding the value of specific stocks due to the unpredictable nature of stock prices. Consequently, there is no business-related subject more talked about than stock market predictions. It is crucial to resolve this issue in a way that benefits buyers and investors because they frequently experience investment losses. Machine learning is useful in developing model for stock value predictions. We are utilizing Python and Linear Regression, one of the Machine Learning statistical techniques for predictive analysis, to create a stock price prediction website in order to address this issue. Our study primarily focuses on the NIFTY50 index's performance in distributed lag with the purpose of predicting stock prices in the Indian stock market. Several useful characteristics of the NIFTY50 lag index were extracted by means of a genetic algorithm. After that, we uncovered hidden correlations between the stock index and a given stock's trend by using the linear regression classifier. For the purpose of testing our approach, we used it to forecast the future of three distinct equities. In comparison to state-of-the-art forecasting methodologies, our experimental results demonstrated an accuracy of 82.55%. For the purpose of predicting daily changes in stock prices, the NIFTY50 stock index proved to be useful.

Non-contact Respiration Monitoring Using Bio- Radar Sensor Based on Linear Regression Classifier

Tuberculosis (TB) is an infectious disease that mainly attacks the lungs, caused by the bacterium Mycobacterium tuberculosis. To reduce its spread, hospitals use special rooms for TB patients and health workers follow strict Standard Operating Procedures (SOP). Recent advances in medical technology have led to the development of contactless respiratory monitoring techniques, such as bio-radar sensors that utilize the Doppler principle to detect lung movement. This research aims to explore the application of bio-radar sensors for contactless respiratory rate monitoring and then combine it with machine learning methods, specifically using linear regression algorithms, to translate bio-radar output into measurable respiratory rate values. By training a regression model using a processed raw data set to identify inspiration and expiration, where 1 is inspiration and 0 is expiration. To test the performance of the contactless breathing module, it was compared to a patient monitor. The module and comparison tool were run simultaneously with 10 measurement distance points for 10 patients or respondents with each distance point taken three times. The data that has been obtained from the results of comparisons between modules and comparison tools is entered into machine learning data analysis techniques, namely accuracy, precision and recall. The accuracy results were 74.9%, precision 71.4% and recall 83.3%. This research has proven that bio radar can be used to detect lung movement.

Data-driven learning-based classification model for mitigating false data injection attacks on dynamic line rating systems

The increasing need to explore electric power grid expansion technologies like dynamic line rating (DLR) systems and their dependence on real-time weather data for system planning necessitates research into false data injection attacks (FDIA) on cyber-physical power systems (CPS). This study aims to develop a robust machine learning model to mitigate FDIA in DLR systems, focusing on statistical data processing, feature ranking, selection, training, validation and evaluation. It synthesises z-score and other statistical analyses with minimum redundancy maximum relevance (MR-MR) feature ranking and selection algorithm to improve model performance and generalisation of binary generalised linear model logistic regression (BGLM-LR) and other machine learning classification algorithms. The resulting models formed with BGLM-LR, Gaussian naïve Bayes (GNB), linear support vector machine (LSVM), wide neural network (WNN), and decision tree (DT) were trained and tested with 10-year hourly DLR history data features. The evaluation of the models on unseen data revealed enhanced validation and testing accuracies after the MR-MR feature ranking and selection. BGLM-LR, GNB, LSVM, and WNN showed promising performance for mitigating FDIA. However, the study identifies DT’s limitations as overfitting and lacking generalisation in FDIA mitigation. z-score-MR-MR-BGLM-LR and z-score-MR-MR-LSVM models exhibited outstanding performances with zero false negative rates highlighting the significance of feature ranking and selection. Still, the z-score-MR-MR-BGLM-LR combination exhibits the highest marginal improvement from training to testing, the lowest training and validation time and a perfect area under curve (AUC) of the receiver operating characteristics making it the best choice in mitigating FDIA when computational resources are limited.

An assessment of pluvial hazard in South Jakarta based on land-use/cover change from 2016 to 2022

South Jakarta is a megapolitan city that is directly affected by Land Use Cover Change (LUCC). One of the impacts that arise is urban flooding, otherwise known as pluvial floods. More work is needed to evaluate the effect of LUCC on the increase of pluvial floods in South Jakarta. In this study, the runoff coefficient value (C) caused by LUCC is determined and the hazard risk of pluvial floods is evaluated. The analysis uses linear regression classification of LUCC using GIS software and carries out hazard and risk analysis based on Minister of Public Works Regulation Number 12 of 2014. Based on the rational method flood discharge formula, the higher C value result in the greater flow rate in a watershed. To simplify the calculation, the C equivalent value used is the C value that represent the watershed. The growth of the runoff coefficient (C) in South Jakarta is 1%–1.3% per year. It is found that the rate of increase in the runoff coefficient (C) is the same as the population growth rate of DKI Jakarta, which is around 1.3% per year. The increasing of runoff coefficient also increases the risk of pluvial flooding in locations that are prone to flooding. Furthermore, the hazard risk study found that there are 2 locations with a moderate level on the hazard risk index, namely, Ciledug Seskoal and Dharmawangsa Taman Gajah. This study can be used by government agencies as a basis to construct the spatial prioritization framework for pluvial flood mitigation.

MSFSS: A whale optimization-based multiple sampling feature selection stacking ensemble algorithm for classifying imbalanced data

<abstract> <p>Learning from imbalanced data is a challenging task in the machine learning field, as with this type of data, many traditional supervised learning algorithms tend to focus more on the majority class while damaging the interests of the minority class. Stacking ensemble, which formulates an ensemble by using a meta-learner to combine the predictions of multiple base classifiers, has been used for solving class imbalance learning issues. Specifically, in the context of class imbalance learning, a stacking ensemble learning algorithm is generally considered to combine with a specific sampling algorithm. Such an operation, however, might suffer from suboptimization problems as only using a sampling strategy may make it difficult to acquire diverse enough features. In addition, we also note that using all of these features may damage the meta-learner as there may exist noisy and redundant features. To address these problems, we have proposed a novel stacking ensemble learning algorithm named MSFSS, which divides the learning procedure into two phases. The first stage combined multiple sampling algorithms and multiple supervised learning approaches to construct meta feature space by means of cross combination. The adoption of this strategy satisfied the diversity of the stacking ensemble. The second phase adopted the whale optimization algorithm (WOA) to select the optimal sub-feature combination from the meta feature space, which further improved the quality of the features. Finally, a linear regression classifier was trained as the meta learner to conduct the final prediction. Experimental results on 40 benchmarked imbalanced datasets showed that the proposed MSFSS algorithm significantly outperformed several popular and state-of-the-art class imbalance ensemble learning algorithms. Specifically, the MSFSS acquired the best results in terms of the F-measure metric on 27 datasets and the best results in terms of the G-mean metric on 26 datasets, out of 40 datasets. Although it required consuming more time than several other competitors, the increment of the running time was acceptable. The experimental results indicated the effectiveness and superiority of the proposed MSFSS algorithm.</p> </abstract>

Contributions of each of the four swimming strokes to elite 200-400 individual medley swimming performance in short and long course competitions.

The relative contribution of each of the four strokes to performance, and whether these contributions differ substantially between short course and long course competitions is unclear. To clarify these issues the aim of this study was to assess the strokes that have more influence on the performance in the 200 and 400m IM swimming performances of elite male and female swimmers, participating in major events: Olympic Games (OG) and World Championship (WC) in short-course and long-course from 2012 to 2021. Data from 1,095 swimmers (501 women and 594 men) who competed in 200 and 400-m IM were obtained with a minimum level of 800 FINA points. Linear regression modelling and classification trees were employed to quantify differences between strokes and short/long course swimming. Regression analysis indicated that breaststroke (β=-0.191; p<0.000) and backstroke (β=-0.185; p<0.000) had a bigger effect on IM performance, with butterfly (β=-0.101; p<0.000) having a lesser impact. The classification trees showed threshold performance standards in terms of 50-m times in form-stroke events must be fulfilled to attain medal-winning performances. These form-stroke standards represent important milestones for designing medal-oriented training strategies for both 200 IM and 400 m IM. Achieving a medallist position in 200 and 400 m IM requires obtaining specified lap times in butterfly, breaststroke and backstroke for males and females in long-course competitions, and breaststroke and backstroke for short-course competitions. The OG presents more exigent demands of lap times in butterfly, crawl and backstroke for IM swimmers.

Prediction of short‐term MCI‐to‐AD conversion using multi‐modal neuroimaging features, and demographics

AbstractBackgroundMulti‐modality data can achieve better classification and regression performance than the use of only the single modality data. For this reason, we observed the potential of multiple data for early dementia conversion.MethodIn this study, the conversion group was defined as cases where MCI patients converted to dementia between 2 and 4 years, and T1‐weighted, T2 FLAIR, and Amyloid PET images were acquired from 4 domestic hospitals and ADNI. 114 volume features, WMH volume, and Fazekas scale were acquired from T1, T2 FLAIR images, and SUVR was calculated from amyloid PET. In addition to image features, age, sex, MMSE, and ApoE genotype were used. The 206 cases dataset was divided in a stratified way into a training set (80%) and testing set (20%), keeping the sample percentage of each class in both sets. We wanted to check the performance of each model on our dataset. The models used for this purpose were Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Linear Regression Classifier (LR), Gradient Boosting Model (GBM), Extreme Gradient Boosting (XGB). We trained and tuned each model, set up a grid search through hyperparameters to select a model that generalized well. The metrics used to evaluate model performance were BA, SE, SP, and AUC.ResultTable 2 and 3 present the obtained results of all ML models for the two‐class classification task using 10‐fold cross‐validation and testing. In the case of 10‐fold cross‐validation, the model with highest mean BA was the SV model with 0.927. However, in the case of testing, we can see in Table 3 that the model with the highest BA, SE, and SP obtained was the GBM model, which was 0.917, 0.900, and 0.933, respectively. When ensemble was performed using the top3 models, BA, SE, SP, and AUC of the ensemble model were 0.917, 0.900, 0.933, and 0.963, respectively.ConclusionMuti‐modal neuroimaging features with minimal demographic information showed reliable results in predicting the converters and non‐converters between 2 and 4 years. This study suggests it could be utilized for future clinical trials to provide a high‐risk group for dementia conversion in advance.

Body circumference fitting model based on coupled linear regression and body type classification

Body circumference fitting model is a key technology for personalization in smart manufacturing by the apparel industry. First, the paper collected front and side images plus manual measurement data of 122 young men. Second, through contour recognition and feature point detection of the collected images, the width and thickness of each body part plus the height were estimated. Then, a correlation analysis of these known factors was conducted. The paper proposed that weight was included in initial regression model for circumferences as an additional independent variable along with width and thickness. Finally, according body type classification, a multivariate regression model was established. After validation, the values predicted by the multivariate regression model were within ±1.5 cm of the manual values for 90.9% of the individuals on average. The paper provides theoretical support for body circumference fitting and may also be beneficial to the development of the smart manufacturing of garments.

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis.

Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the efficacy of models constructed using classification methods and explores the effectiveness of relevant features that are derived using various machine-learning techniques. Five feature selection approaches, namely Chi-Square (χ2), Exploratory Factor Score (EFS), tree-based Recursive Feature Elimination (RFE), gradient-based RFE, and Linear Regression Coefficient, were employed to determine the optimal feature set. Nine classifiers, namely K-Nearest Neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GBC), Linear Regression (LR), AdaBoost, Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM), were utilized to evaluate the performance. The χ2 method achieved accuracy, registering 91.56% precision, 91.04% recall, and 92.65% f-score when aligned with the LR classifier. Moreover, the results underscore that amalgamating over-sampling with Synthetic Minority Over-sampling Technique (SMOTE), RFE, and 10-fold cross-validation markedly elevates the detection accuracy for αT patients. Notably, the Gradient Boosting Classifier (GBC) achieves 93.46% accuracy, 93.89% recall, and 92.72% F1 score.

Metabolomic profiling of preterm birth in pregnant women living with HIV

BackgroundPreterm birth is a leading cause of death in children under the age of five. The risk of preterm birth is increased by maternal HIV infection as well as by certain antiretroviral regimens, leading to a disproportionate burden on low- and medium-income settings where HIV is most prevalent. Despite decades of research, the mechanisms underlying spontaneous preterm birth, particularly in resource limited areas with high HIV infection rates, are still poorly understood and accurate prediction and therapeutic intervention remain elusive.ObjectivesMetabolomics was utilized to identify profiles of preterm birth among pregnant women living with HIV on two different antiretroviral therapy (ART) regimens.MethodsThis pilot study comprised 100 mother-infant dyads prior to antiretroviral initiation, on zidovudine monotherapy or on protease inhibitor-based antiretroviral therapy. Pregnancies that resulted in preterm births were matched 1:1 with controls by gestational age at time of sample collection. Maternal plasma and blood spots at 23–35 weeks gestation and infant dried blood spots at birth, were assayed using an untargeted metabolomics method. Linear regression and random forests classification models were used to identify shared and treatment-specific markers of preterm birth.ResultsClassification models for preterm birth achieved accuracies of 95.5%, 95.7%, and 80.7% in the untreated, zidovudine monotherapy, and protease inhibitor-based treatment groups, respectively. Urate, methionine sulfone, cortisone, and 17α-hydroxypregnanolone glucuronide were identified as shared markers of preterm birth. Other compounds including hippurate and N-acetyl-1-methylhistidine were found to be significantly altered in a treatment-specific context.ConclusionThis study identified previously known as well as novel metabolomic features of preterm birth in pregnant women living with HIV. Validation of these models in a larger, independent cohort is necessary to ascertain whether they can be utilized to predict preterm birth during a stage of gestation that allows for therapeutic intervention or more effective resource allocation.

Moth–Flame Optimization based ensemble classification for intrusion detection in intelligent transport system for smart cities

The number of vehicles travelling on the road has increased tremendously over the past few decades throughout the globe. The advent of Intelligent Transport System (ITS) enabled the vehicles to be managed intelligently. However, the ITS network can be intruded by malicious users to access the sensitive information. So a strong Intrusion Detection System (IDS) is required to safeguard the ITS network. Due to the huge volume of data generated by the sensors in the vehicles in ITS, there is a dire need to extract the most important features from the ITS data for classification phase in IDS. Even though, many researchers have worked on building an IDS system in ITS, not much work is done on addressing the aforementioned issue. To address this problem, we have utilized the Moth–Flame Optimization (MFO) in this work. In the proposed work, a MFO based ensemble machine learning model is proposed to classify the IDS dataset in ITS. Firstly the IDS dataset is normalized using the standard-scaler method. Then optimal features from the IDS dataset are chosen by MFO. These optimal features are trained by an ensemble classifier comprising of linear regression, random forest and XGBoost classification algorithms for intelligent decision making. The performance of the proposed model is compared against several state of the art meta-heuristic algorithms based on ensemble models which achieved a superior accuracy and recall of 100%, and precision of 99.5% on shellcode attacks.

Classification and regression model to manage the hospitalization for laparoscopic cholecystectomy

Gallstone disease (GD) is one of the most common morbidities in the world. Laparoscopic Cholecystectomy (LC) is currently the gold standard, performed in about 96% of cases. The most affected groups are the elderly, who generally have higher pre- and post-operative morbidity and mortality rates and longer Length of Stay (LOS). For this reason, several indicators have been defined to improve quality and efficiency and contain costs. In this study, data from patients who underwent LC at the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital of Salerno in the years 2010–2020 were processed using a Multiple Linear Regression (MLR) model and Classification algorithms in order to identify the variables that most influence LOS. The results of the 2352 patients analyzed showed that pre-operative LOS and Age were the independent variables that most affected LOS. In particular, MLR model had a R2 value equal to 0.537 and the best classification algorithm, Decision Tree, had an accuracy greater than 83%. In conclusion, both the MLR model and the classification algorithms produced significant results that could provide important support in the management of this healthcare process.

Neural network representations for the inter- and intra-class common vector classifiers

Common Vector Approach (CVA) is a known linear regression-based classifier, which also enables an extension to inter-class discrimination, known as the Discriminative Common Vector Approach (DCVA). The characteristics of linear regression classifiers (LRCs) enable the possibility of a schematic implementation that is similar to the neuron model of artificial neural networks (ANNs). In this work, we explore this schematic similarity to come up with an ANN representation for both CVA and DCVA. The new representation eliminates the need for projection matrices in its implementation, hence significantly reduces the memory requirements and computational complexities of the processes. Furthermore, since the new representation is in a neural style, it is expected to provide a solid and intriguing extension of CVA (and DCVA) by further incorporating adaptation or activation processes to the already successful CVA-based classifiers.

A Model-Free Feature Selection Technique of Feature Screening and Random Forest-Based Recursive Feature Elimination

This paper studies data with mass features, commonly observed in applications such as text classification and medical diagnosis. We allow data to have several structures without requiring a specific model and propose an efficient model-free feature selection procedure. The proposed method can work with various types of datasets. We demonstrate that this method has several desirable properties, including high accuracy, model-free, and computational efficiency and can be applied to practical problems with different modelings. We prove that the proposed method achieves selection consistency and L 2 consistency under mild regularity conditions. We conduct simulations on various datasets, including data generated from the generalized linear model, additive model, Poisson regression, and binary classification model. These simulations illustrate the superior performance of the proposed method compared to other existing methods across different model settings. In addition, we apply our method to two real examples, the Tecator dataset and the Daily Demand Orders dataset, both of which are continuous and high dimensional. In both cases, our method consistently achieves high accuracy in prediction and model selection.

Exploring the Feasibility of Machine Learning to Predict Risk Stratification Within 3 Months in Chest Pain Patients with Suspected NSTE-ACS

ObjectiveWe aimed to assess the feasibility and superiority of machine learning (ML) methods to predict the risk of Major Adverse Cardiovascular Events (MACEs) in chest pain patients with NSTE-ACS. MethodsEnrolled chest pain patients were from two centers, Beijing Anzhen Emergency Chest Pain Center Beijing Bo’ai Hospital, China Rehabilitation Research Center. Five classifiers were used to develop ML models. Accuracy, Precision, Recall, F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system. Ultimately, ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs. ResultsAccording to learning metrics, ML models constructed by different classifiers were superior over HEART (History, ECG, Age, Risk factors, & Troponin) scoring system when predicting acute myocardial infarction (AMI) and all-cause death. However, according to ROC curves and AUC, ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI. Among the five ML algorithms, Linear support vector machine (SVC), Naïve Bayes and Logistic regression classifiers stood out with all Accuracy, Precision, Recall and F-Measure from 0.8 to 1.0 for predicting any event, AMI, revascularization and all-cause death (vs. HEART ≤ 0.78), with AUC from 0.88 to 0.98 for predicting any event, AMI and revascularization (vs. HEART ≤ 0.85). ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome (ACS), abnormal electrocardiogram (ECG), elevated hs-cTn I, sex and smoking were risk factors of MACEs. ConclusionCompared with HEART risk scoring system, the superiority of ML method was demonstrated when employing Linear SVC classifier, Naïve Bayes and Logistic. ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.

Diagnosis of Obstructive Sleep Apnea Using Feature Selection, Classification Methods, and Data Grouping Based Age, Sex, and Race.

Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3-7% of males and 2-5% of females. In the United States alone, 50-70 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, thereby leading to adverse effects such as daytime sleepiness, cognitive impairment, and reduced concentration. It also contributes to an increased risk of cardiovascular conditions and adversely impacts patient overall quality of life. As a result, numerous researchers have focused on developing automated detection models to identify OSA and address these limitations effectively and accurately. This study explored the potential benefits of utilizing machine learning methods based on demographic information for diagnosing the OSA syndrome. We gathered a comprehensive dataset from the Torr Sleep Center in Corpus Christi, Texas, USA. The dataset comprises 31 features, including demographic characteristics such as race, age, sex, BMI, Epworth score, M. Friedman tongue position, snoring, and more. We devised a novel process encompassing pre-processing, data grouping, feature selection, and machine learning classification methods to achieve the research objectives. The classification methods employed in this study encompass decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), and subspace discriminant (Ensemble) classifiers. Through rigorous experimentation, the results indicated the superior performance of the optimized kNN and SVM classifiers for accurately classifying sleep apnea. Moreover, significant enhancements in model accuracy were observed when utilizing the selected demographic variables and employing data grouping techniques. For instance, the accuracy percentage demonstrated an approximate improvement of 4.5%, 5%, and 10% with the feature selection approach when applied to the grouped data of Caucasians, females, and individuals aged 50 or below, respectively. Furthermore, a comparison with prior studies confirmed that effective data grouping and proper feature selection yielded superior performance in OSA detection when combined with an appropriate classification method. Overall, the findings of this research highlight the importance of leveraging demographic information, employing proper feature selection techniques, and utilizing optimized classification models for accurate and efficient OSA diagnosis.

Potential risk genes for primary Sjogren's syndrome from a meta-analysis by linear regression and random forest classification

Potential risk genes for primary Sjogren's syndrome from a meta-analysis by linear regression and random forest classification