Effective Machine Learning Algorithm Research Articles

Mutual Clustered Redundancy and Composite Learning for Intrusion Detection Systems

In the area of cyber space security, intrusion detection is a challenging task which aims at the provision of security from various malicious attacks. Hence, this paper proposes a two-stage hybrid intrusion detection system (IDS) mechanism to identify between normal and attack activities. The proposed mechanism is an integrated form of two simple and effective machine learning algorithms; they are support vector machine (SVM) and composite extreme learning machine (CELM). The first stage aims to distinguish the normal activities from abnormal activities and employed SVM. Next, the second stage employs CELM for the detection of different types of attacks . Further, aiming over training data, a clustering followed by duplicate connections removal and duplicate features removal is accomplished through fuzzy C-means clustering, correlation, and mutual information respectively. The proposed method applied eventually on the standard benchmark dataset NSL-KDD and the real modern UNSW-NB15 dataset. The performance analysis validates through accuracy, false alarm rate and computational time.

A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders

Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder that affects the everyday life of affected patients. Though it is considered hard to completely eradicate this disease, disease severity can be mitigated by taking early interventions. In this paper, we propose an effective framework for the evaluation of various Machine Learning (ML) techniques for the early detection of ASD. The proposed framework employs four different Feature Scaling (FS) strategies i.e., Quantile Transformer (QT), Power Transformer (PT), Normalizer, and Max Abs Scaler (MAS). Then, the feature-scaled datasets are classified through eight simple but effective ML algorithms like Ada Boost (AB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Our experiments are performed on four standard ASD datasets (Toddlers, Adolescents, Children, and Adults). Comparing the classification outcomes using various statistical evaluation measures (Accuracy, Receiver Operating Characteristic: ROC curve, F1-score, Precision, Recall, Mathews Correlation Coefficient: MCC, Kappa score, and Log loss), the best-performing classification methods, and the best FS techniques for each ASD dataset are identified. After analyzing the experimental outcomes of different classifiers on feature-scaled ASD datasets, it is found that AB predicted ASD with the highest accuracy of 99.25%, and 97.95% for Toddlers and Children, respectively and LDA predicted ASD with the highest accuracy of 97.12% and 99.03% for Adolescents and Adults datasets, respectively. These highest accuracies are achieved while scaling Toddlers and Children with normalizer FS and Adolescents and Adults with the QT FS method. Afterward, the ASD risk factors are calculated, and the most important attributes are ranked according to their importance values using four different Feature Selection Techniques (FSTs) i.e., Info Gain Attribute Evaluator (IGAE), Gain Ratio Attribute Evaluator (GRAE), Relief F Attribute Evaluator (RFAE), and Correlation Attribute Evaluator (CAE). These detailed experimental evaluations indicate that proper finetuning of the ML methods can play an essential role in predicting ASD in people of different ages. We argue that the detailed feature importance analysis in this paper will guide the decision-making of healthcare practitioners while screening ASD cases. The proposed framework has achieved promising results compared to existing approaches for the early detection of ASD.

Experimental Studies for Glass Light Transmission Degradation in Solar Cells Due to Dust Accumulation Using Effective Optical Scattering Parameters and Machine Learning Algorithm

Environmental impacts influence solar cell performance significantly. A harsh environment may cause temperature, wind speed, or humidity uncertainties. In the case of photovoltaic systems in desert or semidesert areas, dust accumulation critically impacted solar cell degradation. Herein, we developed a novel machine-learning-based optical model that estimates solar cell efficiency's degradation because of dust accumulation. Glass substrates were directed to dust for 16 weeks with a weekly characterization procedure. Samples were morphologically and optically characterized to be seeded into the scattering model. Morphological characterization measurements were conducted using SEM and EDX-mapping to explore the internal composition of dust particles. Optically, the dusty glass substrate sample's transmission spectra were measured using a UV-Vis spectrometer. Refractive index, thickness, and scattering particle radius have been extracted as effective parameters with fitting accuracy of 95%. The extracted dataset is then inputted into a machine-learning model to predict the transparency degradation in the glass substrate. Finally, predicted data is validated against the actual degraded monocrystalline cell, showing 89.4% of matching.

EEG based Schizophrenia Detection using SPWVD-ViT Model

Schizophrenia is a typical neurological disease that affects patients’ mental state, and daily behaviours. Combining image generation techniques with effective machine learning algorithms may accelerate treatment process, and possible early alert systems prevents diseases from reaching out crucial phase. The purpose of current study is to develop an automated EEG based schizophrenia detection with the Vision Transformer (ViT) model using Smoothed Pseudo Wigner Ville Distribution (SPWVD) time-frequency input images. EEG recordings from 35 schizophrenia (sch) and 35 healthy conditions (hc) are analyzed. We have used 5-fold cross validation for evaluation and testing of the method. Classification task is carried out as subject-independent and subject-dependent method. We reached out overall accuracy of 87% for subject-independent and 100% for subject-dependent approach for binary classification. While ViT has ben extensively used in Natural Language Processing (NLP) field, dividing input images within a sequence of embedded image patches via. transformer encoder is a practical way for medical image learning and developing diagnostic tools. SPWVD-ViT model is recommended as a disease detection tool not only for schizophrenia but other neurological symptoms.

Efficient Model for Coronary Artery Disease Diagnosis: A Comparative Study of Several Machine Learning Algorithms.

Background In today's industrialized world, coronary artery disease (CAD) is one of the leading causes of death, and early detection and timely intervention can prevent many of its complications and eliminate or reduce the resulting mortality. Machine learning (ML) methods as one of the cutting-edge technologies can be used as a suitable solution in diagnosing this disease. Methods In this study, different ML algorithms' performances were compared for their effectiveness in developing a model for early CAD diagnosis based on clinical examination features. This applied descriptive study was conducted on 303 records and overall 26 features, of which 26 were selected as the target features with the advice of several clinical experts. In order to provide a diagnostic model for CAD, we ran most of the most critical classification algorithms, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), Logistic Regression (LR), J48, Random Forest (RF), K-Nearest Neighborhood (KNN), and Naive Bayes (NB). Seven different classification algorithms with 26 predictive features were tested to cover all feature space and reduce model error, and the most efficient algorithms were identified by comparison of the results. Results Based on the compared performance metrics, SVM (AUC = 0.88, F-measure = 0.88, ROC = 0.85), and RF (AUC = 0.87, F-measure = 0.87, ROC = 0.91) were the most effective ML algorithms. Among the algorithms, the KNN algorithm had the lowest efficiency (AUC = 0.81, F-measure = 0.81, ROC = 0.77). In the diagnosis of coronary artery disease, machine learning algorithms have played an important role. Proposed ML models can provide practical, cost-effective, and valuable support to doctors in making decisions according to a good prediction. Discussion. It can become the basis for developing clinical decision support systems. SVM and RF algorithms had the highest efficiency and could diagnose CAD based on patient examination data. It is suggested that further studies be performed using these algorithms to diagnose coronary artery disease to obtain more accurate results.

Prediction of water quality indexes with ensemble learners: Bagging and boosting

One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models.

FEDERATED LEARNING FOR 5G MOBILE NETWORKS

This paper investigates the main advantages of using Federated Learning (FL) for sharing experiences between intelligent devices in the environment of 5th generation mobile communication networks. This approach makes it possible to build effective machine learning algorithms using confidential data, the loss of which may be undesirable or even dangerous for users. Therefore, for the tasks where the confidentiality of the data is required for processing and analysis, we suggest using Federated Learning (FL) approaches. In this case, all users' personal information will be processed locally on their devices. FL ensures the security of confidential data for subscribers, allows mobile network operators to reduce the amount of redundant information in the radio channel, and also allows optimizing the functioning of the mobile network. The paper presents a three-level model of integration of Federated Learning into the mobile network and describes the main features of this approach, as well as experimental studies that demonstrate the results of the proposed approach.

Power Transformer Fault Prediction using Naive Bayes and Decision tree based on Dissolved Gas Analysis

Power transformers are the basic elements of the power grid, which is directly related to the reliability of the electrical system. Many techniques were used to prevent power transformer failures, but the Dissolved Gas Analysis (DGA) remains the most effective one. Based on the DGA technique, this paper describes the use of two of the most effective machine learning algorithms: Naive Bayes and Decision Tree for the identification of power transformer’s faults. In our investigation, 9 different input vectors have been developed from widely known DGA techniques. 481 samples have been used and 6 types of faults have been considered. The evaluation result of the implementation of the proposed methods shows an effectiveness of 86.25% in power transformer’s fault recognition.

Comparision of Different Machine Learning Algorithms to Predict the Diagnostic Accuracy Parameters of Celiac Serological Tests

Celiac disease; is an autoimmune digestive system disease characterized by chronic intestinal inflammation and villus antrophy and triggered by dietary gluten genetically susceptible individuals. Diagnosis is based on serological tests and small bowel biopsy. Because of the diversity in the clinical features of the disease, various patient profile and the non-standardized serological tests, it is difficult to diagnose the celiac disease. Sensitivity, specificity, positive and negative predictive values are important parameters for the accuracy of the tests and they are missing in some clinicial studies. It is difficult do standardize the tests with these missing values for clinicians. The aim of this study is to train different machine learning algorithms and to test their performance in prediction of the diagnostic accurary parameters of celiac serological tests. Decision trees are effective machine learning algorithms for predicting potential covariates with %88,7 accuracy.

Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases.

In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.

Towards learning trustworthily, automatically, and with guarantees on graphs: An overview

The increasing digitization and datification of all aspects of people’s daily life, and the consequent growth in the use of personal data, are increasingly challenging the current development and adoption of Machine Learning (ML). First, the sheer complexity and amount of data available in these applications strongly demands for ML algorithms that can be trained directly on complex structures, which can be naturally described by graphs. In fact, graphs inherently capture information about entities, their attributes, and relationships between them. Directly applying ML to graphs relieves domain experts and data scientists from the challenging and time-consuming problem of designing a suitable vector-based data representation used by classical ML techniques. Second, ML algorithms should not only be designed to achieve high technical and functional standards; as the automated decisions provided by these algorithms can have a relevant impact on people’s lives, their behavior has to be aligned with the values and principles of individuals and society. This demands for designing automated algorithms that we, as humans, can trust, fulfilling the requirements of fairness, robustness, privacy, and explainability. Third, designing effective ML algorithms requires skills and expertise developed at different levels. This substantially hinders the democratization and widespread availability of such technology for society at large, which in turn demands for improving the level of automatization and systematization of their design process, while also providing guarantees on their performance. For this reason, this paper provides an overview of the current works focused towards learning trustworthily, automatically, and with guarantees on graphs.

A Hybrid Machine Learning Model to Predict Heart Disease Accurately

Objective: To propose the most effective machine learning algorithm for predicting cardiac problems. Methods: The dataset used for this study is “heart” which was taken from www.kaggle.com. The heart dataset contains 13 features and a target variable. It is divided into 70 percent training set and 30 percent testing set. K-Fold cross-validation is used in this study for model evaluation and model selection. The K value chosen is ten. A Hybrid Ensemble machine learning model is built using a heterogeneous collection of weak learners in this work. To construct a hybrid ensemble model, weak learners such as “Logistic Regression”, “Decision Tree”, “Support Vector Machine”, “KNearest Neighbor”, and “Naive Bayes” are used. Normally, in an ensemble model, a homogeneous group of weak learners is utilized, however in this study, a heterogeneous group of weak learners is used. The parameter used in this study is accuracy. Accuracy of all the weak learners is found and compared with the hybrid ensemble model. Findings: Weak machine learning models are combined to create an ensemble model. The ”Hybrid Ensemble model” has a 98 percent accuracy rate and outperforms all weak learners such as “Logistic Regression”, “Decision Tree”, “Support Vector Machine”, “K-Nearest Neighbor”, and “Naive Bayes”. Novelty and applications : For the prediction of heart problems, the hybrid ensemble model is recommended since it extracts more accurate and valuable data from huge amounts of data, making prediction easier for physicians. Keywords: Machine Learning; Ensemble Model; Weak Learners; Homogeneous; Heterogeneous

Machine learning applications in cardiac computed tomography: a composite systematic review.

Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.

ПЕРСПЕКТИВЫ ИСПОЛЬЗОВАНИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ДИАГНОСТИКЕ COVID-19

The article discusses the possibilities of using arti cial intelligence to detect COVID-19 disease. Based on the research, the best practices and potentially effective approaches to using effective machine and deep learning algorithms are presented. Various groups of initial data have been selected that are suitable for different tasks, on the basis of which the trained model can predict a fairly correct result. The analysis of modern Russian and foreign scienti c articles published over the past year is carried out.

Prediction of rockhead using a hybrid N-XGBoost machine learning framework

The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation. Although the conventional site investigation methods (i.e. borehole drilling) could provide local engineering geological information, the accurate prediction of the rockhead position with limited borehole data is still challenging due to its spatial variation and great uncertainties involved. With the development of computer science, machine learning (ML) has been proved to be a promising way to avoid subjective judgments by human beings and to establish complex relationships with mega data automatically. However, few studies have been reported on the adoption of ML models for the prediction of the rockhead position. In this paper, we proposed a robust probabilistic ML model for predicting the rockhead distribution using the spatial geographic information. The framework of the natural gradient boosting (NGBoost) algorithm combined with the extreme gradient boosting (XGBoost) is used as the basic learner. The XGBoost model was also compared with some other ML models such as the gradient boosting regression tree (GBRT), the light gradient boosting machine (LightGBM), the multivariate linear regression (MLR), the artificial neural network (ANN), and the support vector machine (SVM). The results demonstrate that the XGBoost algorithm, the core algorithm of the probabilistic N-XGBoost model, outperformed the other conventional ML models with a coefficient of determination ( R 2 ) of 0.89 and a root mean squared error (RMSE) of 5.8 m for the prediction of rockhead position based on limited borehole data. The probabilistic N-XGBoost model not only achieved a higher prediction accuracy, but also provided a predictive estimation of the uncertainty. Thus, the proposed N-XGBoost probabilistic model has the potential to be used as a reliable and effective ML algorithm for the prediction of rockhead position in rock and geotechnical engineering.

Predicting Writing Styles of Web-Based Materials for Children's Health Education Using the Selection of Semantic Features: Machine Learning Approach.

BackgroundMedical writing styles can have an impact on the understandability of health educational resources. Amid current web-based health information research, there is a dearth of research-based evidence that demonstrates what constitutes the best practice of the development of web-based health resources on children’s health promotion and education.ObjectiveUsing authoritative and highly influential web-based children’s health educational resources from the Nemours Foundation, the largest not-for-profit organization promoting children’s health and well-being, we aimed to develop machine learning algorithms to discriminate and predict the writing styles of health educational resources on children versus adult health promotion using a variety of health educational resources aimed at the general public.MethodsThe selection of natural language features as predicator variables of algorithms went through initial automatic feature selection using ridge classifier, support vector machine, extreme gradient boost tree, and recursive feature elimination followed by revision by education experts. We compared algorithms using the automatically selected (n=19) and linguistically enhanced (n=20) feature sets, using the initial feature set (n=115) as the baseline.ResultsUsing five-fold cross-validation, compared with the baseline (115 features), the Gaussian Naive Bayes model (20 features) achieved statistically higher mean sensitivity (P=.02; 95% CI −0.016 to 0.1929), mean specificity (P=.02; 95% CI −0.016 to 0.199), mean area under the receiver operating characteristic curve (P=.02; 95% CI −0.007 to 0.140), and mean macro F1 (P=.006; 95% CI 0.016-0.167). The statistically improved performance of the final model (20 features) is in contrast to the statistically insignificant changes between the original feature set (n=115) and the automatically selected features (n=19): mean sensitivity (P=.13; 95% CI −0.1699 to 0.0681), mean specificity (P=.10; 95% CI −0.1389 to 0.4017), mean area under the receiver operating characteristic curve (P=.008; 95% CI 0.0059-0.1126), and mean macro F1 (P=.98; 95% CI −0.0555 to 0.0548). This demonstrates the importance and effectiveness of combining automatic feature selection and expert-based linguistic revision to develop the most effective machine learning algorithms from high-dimensional data sets.ConclusionsWe developed new evaluation tools for the discrimination and prediction of writing styles of web-based health resources for children’s health education and promotion among parents and caregivers of children. User-adaptive automatic assessment of web-based health content holds great promise for distant and remote health education among young readers. Our study leveraged the precision and adaptability of machine learning algorithms and insights from health linguistics to help advance this significant yet understudied area of research.

IDENTIFICATION OF MBTI (MYERS-BRIGGS TYPE INDEX) HUMAN TYPE USING TEXT ON SOCIAL NETWORKS BASED MACHINE LEARNING

This study aims to create a classifier using machine learning methods that determine the psychological type of people based on the text published on social networks according to the Myers-Briggs Type Index classification. The article is based on the implementation of automation of the task of determining the personality type using machine learning, with an explanation for determining the characteristics of a person using the MBTI personality indicator. The methods of logistic regression, random forest and support vector machines were used, and a literary analysis of similar works was carried out. The article presents the progress of research work and the results of each classifier, as well as an analysis of the approaches used. In the context of the current quarantine restrictions, such studies can be of great help in the selection of personnel in companies due to the transition of people to an online format of work, since the study involves determining the personal qualities of people based on their posts in social networks. In this paper, the most effective machine learning algorithms for the Kazakh language, which are simple to use and do not require a lot of computing power, were used and, accordingly, the results of the work for each method were presented, among these methods, the accuracy and reliability of the classifier for the Kazakh language by the method of support vectors were at a good level.

Application of Machine Learning for Fenceline Monitoring of Odor Classes and Concentrations at a Wastewater Treatment Plant.

The development of low-cost sensors, the introduction of technical performance specifications, and increasingly effective machine learning algorithms for managing big data have led to a growing interest in the use of instrumental odor monitoring systems (IOMS) for odor measurements from industrial plants. The classification and quantification of odor concentration are the main goals of IOMS installed inside industrial plants in order to identify the most important odor sources and to assess whether the regulatory thresholds have been exceeded. This paper illustrates the use of two machine learning algorithms applied to the concurrent classification and quantification of odors. Random Forest was employed, which is a machine learning algorithm that thus far has not been used in the field of odor quantification and classification for complex industrial situations. Furthermore, the results were compared with commonly used algorithms in this field, such as artificial neural network (ANN), which was here employed in the form of a deep neural network. Both techniques were applied to the data collected from an IOMS installed for fenceline monitoring at a wastewater treatment plant. Cohen’s kappa and Normalized RMSE are used as specifical performance indicators for classification and regression: the indicators were calculated for the test dataset, and the results were compared with data in the literature obtained in contexts of similar complexity. A Cohen’s kappa of 97% was reached for the classification task, while the best Normalized RMSE, namely 4%, for the interval 20–2435 ouE/m3 was obtained with Random Forest.

Long-term remote sensing monitoring on LUCC around Chaohu Lake with new information of algal bloom and flood submerging

Human settlements are guided by the proximity or availability of a natural resource such as river or lake basins containing set of streams. The harmonious development of human activity and natural conditions along watershed areas needs close attention and in-depth study. In this paper, the urban agglomerations and ecological spaces in the Yangtze River Delta, China, the Chao Lake Basin and its surrounding watershed ecosystem is taken as research subject for its serious environmental degradation problems during social and economic development. This paper adopted an effective machine learning algorithm (kernel-ELM) to extract land use and land /cover information, and to analyze the land use/cover pattern evolution rules of the Chao Lake Basin with long term Landsat imagery. Subsequent studies were then carried out to demonstrate the flood-affected area and its ecological impact in the basin in 2020, to reveal the occupation on land cover types. The results indicate Conclusions are drawn from the experiment results: (1) There has been significant change in cultivated land, forest land and construction land out of six key land cover types with dynamic degree of −10.17%, 4.61, 67.04% respectively. (2) Algae bloom pollution was extracted from pattern classification results and it was up to 15% of the total water area by the year 2018. (3) The occupation on land use/cover types of the flood was revealed. The results prove effective application of remote sensing technology in environmental analysis and planning for data-driven evaluation of governing policy. This work serves as a scientific basis for environmental management and regional planning in the Chao Lake Basin and can be served as a basis and a reference for evaluating an ecological policy and its impact for other economic developing watershed human settlements with ecological issues.

Application of machine learning and laser optical-acoustic spectroscopy to study the profile of exhaled air volatile markers of acute myocardial infarction

Conventional acute myocardial infarction (AMI) diagnosis is quite accurate and has proved its effectiveness. However, despite this, discovering more operative methods of this disease detection is underway. From this point of view, the application of exhaled air analysis for a similar diagnosis is valuable. The aim of the paper is to research effective machine learning algorithms for the predictive model for AMI diagnosis constructing, using exhaled air spectral data. The target group included 30 patients with primary myocardial infarction. The control group included 42 healthy volunteers. The ‘LaserBreeze’ laser gas analyzer (Special Technologies Ltd, Russia), based on the dual-channel resonant photoacoustic detector cell and optical parametric oscillator as the laser source, had been used. The pattern recognition approach was applied in the same manner for the set of extracted concentrations of AMI volatile markers and the set of absorption coefficients in a most informative spectral range 2.900 ± 0.125 µm. The created predictive model based on the set of absorption coefficients provided 0.86 of the mean values of both the sensitivity and specificity when linear support vector machine (SVM) combined with principal component analysis was used. The created predictive model based on using six volatile AMI markers (C5H12, N2O, NO2, C2H4, CO, CO2) provided 0.82 and 0.93 of the mean values of the sensitivity and specificity, respectively, when linear SVM was used.