Effectiveness Of Machine Learning Algorithms Research Articles

Cumulative learning based segmentation aided cell mixtures classification in digital holographic microscopy

The tumor microenvironment consists of several components like tumor cells, normal healthy cells, extracellular matrix, secreted factors, and other non-cellular components. The interaction among these components is crucial for tumor progression and metastasis. Identifying the presence of tumor cells among the normal healthy cells during the initial stages of cancer will aid in its early detection and treatment. One of the main features that differentiates a cancer cell from a normal healthy cell distinctively is its morphology. In this study, the effectiveness of machine learning algorithms is evaluated in classifying cells co-cultured in a Petri dish. To achieve this, digital holographic microscopy is used to capture the quantitative phase images of human dermal fibroblast and A375 human melanoma cancer cells co-cultured at 1:1, 1:2, and 2:1 ratios. Cell segmentation was performed using a cumulative learning approach. Subsequently, morphological and phase-based features were extracted to train the machine learning algorithms for binary classification of cells. Phase-based features in addition to the morphological features were found to provide improved performance of the classifier. The classifier demonstrated an average F1-score of 0.9 during testing.

Mitigating false negatives in imbalanced datasets: An ensemble approach

Imbalanced datasets present a challenge in machine learning, especially in binary classification scenarios where one class significantly outweighs the other. This imbalance often leads to models favoring the majority class, resulting in inadequate predictions for the minority class, specifically in false negatives. In response to this issue, this work introduces the MinFNR ensemble algorithm, designed to minimize False Negative Rates (FNR) in imbalanced datasets. The new approach strategically combines data-level, algorithmic-level, and hybrid-level approaches to enhance overall predictive capabilities while minimizing computational resources using the Set Covering Problem (SCP) formulation. Through a comprehensive evaluation of diverse datasets, MinFNR consistently outperforms individual algorithms, showing its potential for applications where the cost of false negatives is substantial, such as fraud detection and medical diagnosis. This work also contributes to ongoing efforts to improve the reliability and effectiveness of machine learning algorithms in real imbalanced scenarios.

Predictive machine learning algorithms for metabolic syndrome among Tunisian adults

Abstract Background Metabolic syndrome (MS), a cluster of interconnected risk factors for cardiovascular disease and type 2 diabetes, poses a significant health burden globally. In Tunisia, amidst an ongoing epidemiological transition, the prevalence of MS presents a growing concern. Our study aimed to identify the potential risk factors of MS and propose better machine learning (ML) based models for predicting MS among Tunisian adults. Methods Data was sourced from the Tunisian Health Examination Survey THES-2016, encompassing individuals aged 20 years and older. MS was defined based on the criteria set by the International Diabetes Federation. Logistic regression (LR) was employed to determine risk factors for MS. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were calculated. Five ML algorithms (Naïve Bayes, Support Vector Machine (SVM), Artificial Neural Network (ANN), AdaBoost, and Random Forest (RF)) were utilized to predict MS. Performance evaluation was conducted using accuracy, precision, recall, and area under the curve (AUC) metrics. Results Among the 8908 participants, the prevalence of MS was 32.8% (95% CI: 31.4% - 34.6%). LR identified age group 60-69 years (aOR: 18.5, 95% CI: 16.9-19.9, p < 0.001), female sex (aOR: 1.7, 95% CI: 1.2-1.9), sedentariness (aOR: 1.6, 95% CI: 1.5-1.8) and low education level (aOR: 1.2, 95% CI: 1.1-1.4) as significant factors associated with MS. Among ML models tested, AdaBoost had the highest accuracy (89.8%), followed by SVM (89.6%). Naïve Bayes had the highest Recall (98.1%) and the most performant AUC (91.4%), while RF had the highest Precision (70.8%). The ANN had an accuracy of 89%, a precision of 70.4%, a recall of 78.7% and an AUC of 85.1%. Conclusions Our study highlighted the effectiveness of ML algorithms like AdaBoost and SVM in predicting MS among Tunisian adults, offering potential as early detection tools. However, further validation with larger datasets is necessary to solidify their utility in healthcare settings. Key messages • Metabolic syndrome, a complex of cardiovascular risk factors, presents a substantial global public health challenge and is particularly pronounced in Tunisia. • Machine learning algorithms, notably AdaBoost and Support Vector Machine, demonstrate promise in forecasting and addressing metabolic syndrome.in Tunisian adults.

IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection

With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter–wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27% on average compared to various multi-objective feature selection methods, while reducing the number of selected features by 88.76% on average. Meanwhile, IMOABC shows an average improvement of 4.24% in classification accuracy across all datasets, with an average reduction of 76.73% in the number of selected features compared to various traditional methods.

Integrating street-view images to quantify the urban soundscape: Case study of Fuzhou City's main urban areaa).

Soundscapes are an important part of urban landscapes and play a key role in the health and well-being of citizens. However, predicting soundscapes over a large area with fine resolution remains a great challenge and traditional methods are time-consuming and require laborious large-scale noise detection work. Therefore, this study utilized machine learning algorithms and street-view images to estimate a large-area urban soundscape. First, a computer vision method was applied to extract landscape visual feature indicators from large-area streetscape images. Second, the 15 collected soundscape indicators were correlated with landscape visual indicators to construct a prediction model, which was applied to estimate large-area urban soundscapes. Empirical evidence from 98 000 street-view images in Fuzhou City indicated that street-view images can be used to predict street soundscapes, validating the effectiveness of machine learning algorithms in soundscape prediction.

Analysis of urinary non-formed components at home based on machine learning algorithms

Objective: Machine learning can automatically extract valuable insights from vast datasets, predict and classify diseases, and evaluate drug efficacy. To assess the effectiveness of machine learning algorithms in analyzing non-formed components in urine, real medical data were processed and annotated. Methods: Five models, including K-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Machines, and Gaussian distributions, were constructed to quantitatively analyze 12 non-formed urine components, such as vitamin C, white blood cells, and urinary bilirubin. The efficacy of these models was then compared. Results: It was found that the Random Forest model outperformed others, achieving the lowest mean squared error, high recall rate, accuracy, and area under the curve. Conclusions: These findings indicate that machine learning offers significant potential for studying non-formed urine components, potentially enhancing the precision and effectiveness of disease detection and providing valuable support for clinical decision-making.

Comparative analysis of machine learning techniques for detecting potability of water

This research aims to evaluate the effectiveness of machine learning algorithms in determining the potability of water. In the study, a total of 3276 water samples were analyzed for 10 different features that determine the potability of water. Besides that, the study's consideration is to evaluate the impact of trimming, IQR, and percentile methods on the performance of machine learning algorithms. The models were built using nine different classification algorithms (Logistic Regression, Decision Trees, Random Forest, XGBoost, Naive Bayes, K-Nearest Neighbors, Support Vector Machine, AdaBoost, and Bagging Classifier). According to the results, filling the missing data with the population mean and handling outliers with Trimming and IQR methods improved the performance of the models. Random Forest and Decision Tree algorithms were the most accurate in determining the potability of water. The findings of this research are of high importance to sustainable water resource management and serve as a crucial input for the decision-making process on the quality of water. The study also offers an example for researchers working on datasets that contain missing values and outliers.

Machine Learning Approaches for Automated Diagnosis of Cardiovascular Diseases: A Review of Electrocardiogram Data Applications.

Cardiovascular diseases (CVDs) have been identified as the leading cause of mortality worldwide. Electrocardiogram (ECG) is a fundamental diagnostic tool used for the diagnosis and detection of these diseases. The new technological tools can help enhance the effectiveness of ECGs. Machine learning (ML) is widely acknowledged as a highly effective approach in the realm of computer-aided diagnostics. This article presents a review of the effectiveness of ML algorithms and deep-learning algorithms in diagnosing, identifying, and classifying CVDs using ECG data. The review identified relevant studies published in the 5 major databases: PubMed, Web of Science (WoS), Scopus, Springer, and IEEE Xplore; between 2021 and 2023, a total of 30 were chosen for the comprehensive quantitative and qualitative. The study demonstrated that different datasets are available online with data related to CVDs. The various ML techniques are employed for the purpose of classification. Based on our investigation, it has been observed that deep learning-based neural network algorithms, such as convolutional neural networks and deep neural networks, have demonstrated superior performance in the detection of entire record data. Furthermore, deep learning showcases its efficacy even when confronted with a scarcity of data. ML approaches utilizing ECG data exhibit a notable proficiency in the realm of diagnosis, hence holding the potential to mitigate the occurrence of disease-related consequences at advanced stages.

Clustering and Classification of Time Series Sound Data

This scientific article addresses two critical tasks in data analysis—time series classification and clustering, particularly focusing on heart sound recordings. One of the main challenges in analyzing time series lies in the difficulty of comparing different series due to their variability in length, shape, and amplitude. Various algorithms were employed to tackle these tasks, including the Long Short-Term Memory (LSTM), KNN, recurrent neural network for classification and the K-means and DBSCAN methods for clustering. The study emphasizes the effectiveness of these methods in solving classification and clustering problems involving time series data containing heart sound recordings. The results indicate that LSTM is a powerful tool for time series classification due to its ability to retain contextual information over time. In contrast, KNN demonstrated high accuracy and speed in classification, though its limitations became apparent with larger datasets. For clustering tasks, the K-means method proved to be more effective than DBSCAN, showing higher clustering quality based on metrics such as silhouette score, Rand score, and others. The data used in this research were obtained from the UCR Time Series Archive, which includes heart sound recordings from various categories: normal sounds, murmurs, additional heart sounds, artifacts, and extra systolic rhythms. The analysis of results demonstrated that the chosen classification and clustering methods could be effectively used for diagnosing heart diseases. Furthermore, this research opens up new opportunities for further improvement in data processing and analysis methods, particularly in developing new medical diagnostic tools. Thus, this work illustrates the effectiveness of machine learning algorithms for time series analysis and their significance in improving cardiovascular disease diagnosis.

CALLM: Enhancing Clinical Interview Analysis through Data Augmentation with Large Language Models.

The global prevalence of mental health disorders is increasing, leading to a significant economic burden estimated in trillions of dollars. In automated mental health diagnosis, the scarcity and imbalance of clinical data pose considerable challenges for researchers, limiting the effectiveness of machine learning algorithms. To cope with this issue, this paper aims to introduce a novel clinical transcript data augmentation framework by leveraging large language models (CALLM). The framework follows a "patient-doctor role-playing" intuition to generate realistic synthetic data. In addition, our study introduces a unique "Textbook-Assignment-Application" (T-A-A) partitioning approach to offer a systematic means of crafting synthetic clinical interview datasets. Concurrently, we have also developed a "Response-Reason" prompt engineering paradigm to generate highly authentic and diagnostically valuable transcripts. By leveraging a fine-tuned DistilBERT model on the E-DAIC PTSD dataset, we achieved a balanced accuracy of 0.77, an F1-score of 0.70, and an AUC of 0.78 during test set evaluations, which showcase robust adaptability in both Zero-Shot Learning (ZSL) and Few-Shot Learning (FSL) scenarios. We further compare the CALLM framework with other data augmentation methods and PTSD diagnostic works and demonstrates consistent improvements. Compared to conventional data collection methods, our synthetic dataset not only demonstrates superior performance but also incurs less than 1% of the associated costs.

Machine Learning Models for Predicting Mortality in Critically Ill Patients with Sepsis-Associated Acute Kidney Injury: A Systematic Review.

While machine learning (ML) models hold promise for enhancing the management of acute kidney injury (AKI) in sepsis patients, creating models that are equitable and unbiased is crucial for accurate patient stratification and timely interventions. This study aimed to systematically summarize existing evidence to determine the effectiveness of ML algorithms for predicting mortality in patients with sepsis-associated AKI. An exhaustive literature search was conducted across several electronic databases, including PubMed, Scopus, and Web of Science, employing specific search terms. This review included studies published from 1 January 2000 to 1 February 2024. Studies were included if they reported on the use of ML for predicting mortality in patients with sepsis-associated AKI. Studies not written in English or with insufficient data were excluded. Data extraction and quality assessment were performed independently by two reviewers. Five studies were included in the final analysis, reporting a male predominance (>50%) among patients with sepsis-associated AKI. Limited data on race and ethnicity were available across the studies, with White patients comprising the majority of the study cohorts. The predictive models demonstrated varying levels of performance, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.60 to 0.87. Algorithms such as extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) showed the best performance in terms of accuracy. The findings of this study show that ML models hold immense ability to identify high-risk patients, predict the progression of AKI early, and improve survival rates. However, the lack of fairness in ML models for predicting mortality in critically ill patients with sepsis-associated AKI could perpetuate existing healthcare disparities. Therefore, it is crucial to develop trustworthy ML models to ensure their widespread adoption and reliance by both healthcare professionals and patients.

Rock strength prediction based on machine learning: A study from prediction model to mechanism explanation

Rational and effective assessment of rock strength, especially uniaxial compressive strength (UCS), is the crucial basis of rock engineering practices. As computer technology advances, the application of machine learning techniques in predicting rock strength has become increasingly prevalent. However, due to the black-box effect of machine learning algorithms, the prediction process is always indescribable. Therefore, an innovative approach for the interpretable predicting uniaxial compressive strength (UCS) is initially introduced by using Explainable AI to crack the black-box effect of machine learning algorithms and explain the prediction mechanism. The findings reveal that the Xgboost model, optimized with interpretable hyper-parameters, achieves notable success in estimating the uniaxial compressive strength, with an RMSE of 14.69 and an R2 that can reach 0.9117. Additionally, multi-angle comparisons have verified that the robustness of the AOA-Xgboost model surpasses that of the other four hybrid models. The model employs SHAP analysis to measure how much each input factor contributes to the outcomes of the predictions. This work has potential for the prediction of crucial parameters and analysis of internal mechanisms of rock mass in geotechnical engineering.

Predicting oil prices: A comparative analysis of machine learning and image recognition algorithms for trend prediction

This paper investigates the effectiveness of machine learning algorithms, including logistic regression, artificial neural networks, support vector machines, gradient boosting algorithms (XGBoost, ExtraTrees), random forests, and convolutional neural network (CNN) for trend prediction of daily spot oil prices across horizons of 1 to 8 days. We utilize a comprehensive set of features, including technical indicators, financial data, and volatility measures, to predict trends in closing prices. Our results reveal that the CNN model significantly outperforms other algorithms. This superior performance likely stems from CNN’s ability to capture visual patterns in price movements, potentially mimicking how traders identify trends.

Classifying Engineering Students Performance in Online Education with Machine Learning: Affective, Cognitive, and Behavioral Aspects

In the rapidly evolving online learning environment, accurate prediction of student performance plays an important role in improving the quality of education and overall student outcomes. This study investigated the effectiveness of machine learning algorithms in classifying student performance in online courses based on affective, cognitive, and behavioural factors to develop more effective teaching strategies and interventions to support student success. A dataset of 485 engineering students who took Astronomy Physics at a private university in the fall and spring semesters of 2022-2023 was used to train and evaluate six machine learning algorithms: Support vector machines (SVM), K-nearest neighbours (KNN), Naive Bayes (NB) classifier, logistic regression, decision tree, and random forest. The random forest algorithm achieved the highest classification accuracy (87%), correctly classifying 87% of students into one of three performance categories: high, medium, or low. Moreover, the study determined that anxiety and expectations are the most influential factors in increasing student performance in online courses, while the least effective feature is social isolation. The findings suggest that Machine learning can efficiently categorize student performance in online courses, even with a limited set of features, enabling educators to enhance teaching strategies and interventions for better student support.

Effectiveness of machine learning algorithms as a tool to meat traceability system. A case study to classify Spanish Mediterranean lamb carcasses

Establishing the traceability of meat products has been a major focus of food science in recent decades. In this context, recent advances in food nutritional biomarker identification and improvements in statistical technology have allowed for more accurate identification and classification of food products. Moreover, artificial intelligence has now provided a new opportunity for optimizing existing methods to identify animal products. This study presents a comparative analysis of the effectiveness of different machine learning algorithms based on raw data from analyses of organoleptic, sensory and nutritional meat traits to differentiate categories of commercial lamb from an indigenous Spanish breed (Mallorquina breed) obtained from the following production systems: suckling lambs; light lambs from grazing; and light lambs from grazing supplemented with grain. Six machine learning algorithms were evaluated: Artificial Neural Network (ANN), Decision Tree, K-Nearest Neighbours (KNN), Naive Bayes, Multinomial Logistic Regression, and Support Vector Machine (SVM). For each algorithm, we tested three datasets, namely organoleptic traits and sensorial traits (CIELAB colour, water holding capacity, Warner-Bratzler shear force, volatile compounds and trained tasters), and nutritional traits (proximate composition and fatty acid profile). We also tested a combination of all three datasets. All the data were combined into a dataset with 144 variables resulting from the meat characterization, which included 11,232 event records. The ANN algorithm stood out for its high score with each of the three datasets used. In fact, we obtained an overall accuracy of 0.88, 0.83, and 0.88 for the organoleptic-sensory, nutritional, and combined datasets, respectively. The effectiveness of using the SVM algorithm to assign categories of lambs according to its production system performed better with nutritional traits and the full characterization, with performances equal to those obtained with ANN. The KNN algorithm showed the worst performance, with overall accuracies of 0.54 or lower for each of the datasets used. The results of this study demonstrate that machine learning is a useful tool for classifying commercial lamb carcasses. In fact, the ANN and SVM algorithms could be proposed as tools for differentiating categories of lamb production based on the organoleptic, sensory and nutritional characteristics of Mediterranean light lambs' meat. However, in order to improve the traceability methods of lamb meat production systems as a guarantee for consumers and to improve the learning processes used by these algorithms, more studies along these lines with other lamb breeds are required.

Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies

ObjectiveSuicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts.MethodA systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach.ResultsForty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing.The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction.Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts.ConclusionsThe effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification.

Development and validation of a machine learning-based prediction model for sudden cardiac death in the general population: insights from the community cardiovascular cohort

Abstract Background/Introduction Accurately predicting and managing the risk of sudden cardiac death (SCD) in the general population is a formidable challenge. Purpose This study aimed to develop and validate a machine learning (ML)-based prediction model for SCD using data from the Community Cardiovascular Cohort (CCCC) in Taiwan. Methods A community-based cohort study was used for model construction . Four ML algorithms, including extreme gradient boosting (XGBoost), random forests (RF), logistic regression, and deep neural network (DNN), underwent meticulous evaluation. Performance assessment encompassed receiver operating characteristic (ROC) curves, area under the curves (AUC), and accuracy metrics. Significant risk factors were identified through feature importance analysis. Results We employed a random partitioning strategy, allocating 1744 participants (55%) to the training dataset, 952 participants (30%) to the test dataset, and 476 participants (15%) to the validation dataset. In the data training stage, the XGBoost algorithm demonstrated outstanding prediction performance for SCD (AUC: 1.000); In the validation stage, the XGBoost algorithm demonstrated good prediction performance for SCD prediction (AUC: 0.791, 95%CI: 0.617-0.964; Accuracy: 0.971). Using XGBoost-generated predicted probabilities (P(XGB)), we identified individuals with P(XGB) ≥ 0.5 (Group 1) who displayed significantly higher SCD risk (HR 32.0; 95% CI: 9.29-109.9) compared to those with P(XGB) < 0.5 (Group 0). Conclusions Our study demonstrates the effectiveness of ML algorithms, particularly XGBoost, in predicting event of SCD in the general population. The identification of these pivotal risk factors holds profound implications, empowering the identification of high-risk individuals and facilitating the implementation of precisely tailored preventive strategies.Figure 1:KM sruvival curveFigure 2:Feature importance analysis

Using Machine Learning to Measure Conservatism

This study proposes an approach to measure conservatism using machine learning techniques that are not constrained by functional form restrictions. We extend the differential timeliness model to allow for observable characteristics related to conservatism to follow nonlinear relationships. By developing machine learning measures of conservatism, we draw attention to potential benefits and drawbacks and show how its insights complement conventional measures. Our broader goal is to investigate the effectiveness of machine learning algorithms for filtering noise in traditional archival studies and uncovering more complex empirical patterns. This paper was accepted by Suraj Srinivasan, accounting. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.4983 .

Precision healthcare analytics: Integrating ML for automated image interpretation, disease detection, and prognosis prediction

This review paper provides an overview of precision healthcare analytics, focusing on the integration of machine learning (ML) techniques for automating image interpretation, disease detection, and prognosis prediction across various medical imaging modalities, including X-rays, MRIs, and CT scans. Drawing upon existing literature and empirical evidence, we assess the impact of ML-driven automated image interpretation on diagnostic accuracy, highlighting its superiority over traditional methods. Additionally, we examine the effectiveness of ML algorithms in disease detection, emphasizing their potential for early intervention and improved patient outcomes. Furthermore, we explore the prognostic capabilities of ML-based models in forecasting disease progression and guiding treatment strategies. Through a comprehensive synthesis of research findings, we identify key factors influencing the performance of ML algorithms in healthcare applications and discuss strategies for addressing challenges related to data quality, interpretability, and scalability. By critically evaluating current trends and advancements in precision healthcare analytics, this review aims to provide insights into the potential benefits and limitations of ML integration in medical practice, contributing to the ongoing discourse on enhancing patient care and healthcare delivery.

A Machine Learning Model for Evaluation of the Corrosion Inhibition Capacity of Quinoxaline Compounds

Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces.