Analysis Of Machine Learning Algorithms Research Articles

Comparative Analysis of Machine Learning Algorithms for Investigating Myocardial Infarction Complications

Myocardial Infarction (MI) is a condition often leading to death. It arises from inadequate blood flow to the heart, therefore, the classification of MI complications contributing to lethal outcomes is essential to save lives. Machine learning algorithms provide solutions to support the categorization of the MI complication attributes and predict lethal results. This paper compares various machine learning algorithms to classify myocardial infarction complications and to predict fatal consequences. The considered algorithms are Multilayer Perceptron (MLP), Naive Bayes (NB), and Decision Tree (DT). The main objective of this paper is to compare these algorithms in two scenarios: initially using the full dataset once and then using the dataset again, after implementing the WEKA attribute selection algorithm. To accomplish this goal, data from the Krasnoyarsk Interdistrict Clinical Hospital were employed. Results in general revealed that the MLP classifier demonstrated optimal performance regarding the full MI data, whereas the DT classifier emerged as more favorable when the dataset sample size was diminished through an attribute selection algorithm.

Comparative analysis of machine learning algorithms for predicting diarrhea among under-five children in Ethiopia: Evidence from 2016 EDHS.

Background: Diarrhea is a major cause of mortality and morbidity in under-5 children globally, especially in developing countries like Ethiopia. Limited research has used machine learning to predict childhood diarrhea. This study aimed to compare the predictive performance of ML algorithms for diarrhea in under-5 children in Ethiopia. Methods: The study utilized a dataset of 9501 under-5 children from the Ethiopia Demographic and Health Survey 2016. Five ML algorithms were used to build and compare predictive models. The model performance was evaluated using various metrics in Python. Boruta feature selection was employed, and data balancing techniques such as under-sampling, over-sampling, adaptive synthetic sampling, and synthetic minority oversampling as well as hyper parameter tuning methods were explored. Association rule mining was conducted using the Apriori algorithm in R to determine relationships between independent and target variables. Results: 10.2% of children had diarrhea. The Random Forest model had the best performance with 93.2% accuracy, 98.4% sensitivity, 85.5% specificity, and 0.916 AUC. The top predictors were residence, wealth index, and child age, number of living children, deworming, wasting, mother's occupation, and education. Association rule mining identified the top 7 rules most associated with under-5 diarrhea in Ethiopia. Conclusion: The RF achieved the highest performance for predicting childhood diarrhea. Policymakers and healthcare providers can use these findings to develop targeted interventions to reduce diarrhea. Customizing strategies based on the identified association rules has the potential to improve child health and decrease the impact of diarrhea in Ethiopia.

Comparative Analysis of Machine Learning Algorithms Evaluating the Single Nucleotide Polymorphisms of Metabolizing Enzymes with Clinical Outcomes Following Intravenous Paracetamol in Preterm Neonates with Patent Ductus Arteriosus.

Pharmacogenomics has been identified to play a crucial role in determining drug response. The present study aimed to identify significant genetic predictor variables influencing the therapeutic effect of paracetamol for new indications in preterm neonates. Paracetamol has recently been preferred as a first-line drug for managing Patent Ductus Arteriosus (PDA) in preterm neonates. Single Nucleotide Polymorphisms (SNPs) in CYP1A2, CYP2A6, CYP2D6, CYP2E1, and CYP3A4 have been observed to influence the therapeutic concentrations of paracetamol. The purpose of this study was to evaluate various Machine Learning Algorithms (MLAs) and bioinformatics tools for identifying the key genotype predictor of therapeutic outcomes following paracetamol administration in neonates with PDA. Preterm neonates with hemodynamically significant PDA were recruited in this prospective, observational study. The following SNPs were evaluated: CYP2E1*5B, CYP2E1*2, CYP3A4*1B, CYP3A4*2, CYP3A4*3, CYP3A5*3, CYP3A5*7, CYP3A5*11, CYP1A2*1C, CYP1A2*1K, CYP1A2*3, CYP1A2*4, CYP1A2*6, and CYP2D6*10. Amongst the MLAs, Artificial Neural Network (ANN), C5.0 algorithm, Classification and Regression Tree analysis (CART), discriminant analysis, and logistic regression were evaluated for successful closure of PDA. Generalized linear regression, ANN, CART, and linear regression were used to evaluate maximum serum acetaminophen concentrations. A two-step cluster analysis was carried out for both outcomes. Area Under the Curve (AUC) and Relative Error (RE) were used as the accuracy estimates. Stability analysis was carried out using in silico tools, and Molecular Docking and Dynamics Studies were carried out for the above-mentioned enzymes. Two-step cluster analyses have revealed CYP2D6*10 and CYP1A2*1C to be the key predictors of the successful closure of PDA and the maximum serum paracetamol concentrations in neonates. The ANN was observed with the maximum accuracy (AUC = 0.53) for predicting the successful closure of PDA with CYP2D6*10 as the most important predictor. Similarly, ANN was observed with the least RE (1.08) in predicting maximum serum paracetamol concentrations, with CYP2D6*10 as the most important predictor. Further MDS confirmed the conformational changes for P34A and P34S compared to the wildtype structure of CYP2D6 protein for stability, flexibility, compactness, hydrogen bond analysis, and the binding affinity when interacting with paracetamol, respectively. The alterations in enzyme activity of the mutant CYP2D6 were computed from the molecular simulation results. We have identified CYP2D6*10 and CYP1A2*1C polymorphisms to significantly predict the therapeutic outcomes following the administration of paracetamol in preterm neonates with PDA. Prospective studies are required for confirmation of the findings in the vulnerable population.

Comparative Analysis of Machine Learning Algorithms for Detecting Fake News: Efficacy and Accuracy in the Modern Information Ecosystem

In an era where the spread of fake news poses a significant threat to the integrity of the information landscape, the need for effective detection tools is paramount. This study evaluates the efficacy of three machine learning algorithms—Multinomial Naive Bayes, Passive Aggressive Classifier, and Logistic Regression—in distinguishing fake news from genuine articles. Leveraging a balanced dataset, meticulously processed and vectorized through Term Frequency-Inverse Document Frequency (TF-IDF), we subjected each algorithm to a rigorous classification process. The algorithms were evaluated on metrics such as precision, recall, and F1-score, with the Passive Aggressive Classifier outperforming others, achieving a remarkable 0.99 in both precision and recall. Logistic Regression followed with an accuracy of 0.98, while Multinomial Naive Bayes displayed robust recall at 1.00 but lower precision at 0.91, resulting in an accuracy of 0.95. These metrics underscored the nuanced capabilities of each algorithm in correctly identifying fake and real news, with the Passive Aggressive Classifier demonstrating superior balance in performance. The study's findings highlight the potential of employing machine learning techniques in the fight against fake news, with the Passive Aggressive Classifier showing promise due to its high accuracy and balanced precision-recall trade-off. These insights contribute to the ongoing efforts in digital media to develop advanced, ethical, and accurate tools for maintaining information veracity. Future research should continue to refine these models, ensuring their applicability in diverse and evolving news ecosystems.

Comparative Analysis of Machine Learning Algorithms for Sentiment Classification in Amazon Reviews

Sentiment analysis serves as a crucial approach for gauging public opinion across various sectors, including the realm of product reviews. This study focuses on the evaluation of customer sentiments in Amazon reviews using an array of machine learning algorithms—Logistic Regression, Random Forest, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. The methodology employed is robust, characterized by meticulous parameter tuning based on both theoretical and empirical considerations. Comparative analysis of these algorithms, grounded in accuracy and other performance metrics, offers valuable insights into their respective efficacies and limitations for sentiment analysis tasks. The findings of this study contribute to an enhanced understanding of the performance of different machine learning algorithms in sentiment classification and provide a foundation for future research in this domain.

Comparative analysis of machine learning algorithms for breast cancer prediction

Breast cancer is a global health concern, and early diagnosis is crucial for successful treatment. The objective of this paper is to conduct a comparative analysis of machine-learning algorithms for the prediction of breast cancer. This study used the Wisconsin Diagnostic Breast Cancer Dataset. Data preparation, technique selection, and performance evaluation are included in the study. The inquiry begins by comparing malignant and benign instances according to input factors and diagnostic outcomes. Finding components having an inverse relationship to the diagnosis is prioritized. Next, a careful approach is used to choose attributes to improve the dataset for model construction. The preprocessed data trains and optimizes four well-known machine learning algorithms: Random Forest, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression. The models are evaluated for accuracy, precision, recall, F1-score, and ROC curve. This study aimed to evaluate numerous breast cancer prediction systems to determine their strengths and weaknesses. To provide openness and replicability, the study uses the Jupyter Notebook platform, Python, and data analytic tools. The logistic regression model has a test accuracy percentage of 99.26%, surpassing all other models examined in this study. Furthermore, it has a minimum false positive rate (FPR) of 1 and a false negative rate (FNR) of 4. The model exhibits a higher level of precision in comparison to the studies examined in the literature review. This study is crucial for early diagnosis and therapy development. The effects include lower healthcare expenses, better patient outcomes, and better diagnostics. Machine learning has shown promise in fighting breast cancer, boosting its relevance in healthcare.

Machine learning-enhanced detection of minor radiation-induced defects in semiconductor materials using Raman spectroscopy

Radiation damage in semiconductor materials is a crucial concern for electronic applications, especially in the fields of space, military, nuclear, and medical electronics. With the advancements in semiconductor fabrication techniques and the trend of miniaturization, the quality of semiconductor materials and their susceptibility to radiation-induced defects have become more important than ever. In this context, machine learning (ML) algorithms have emerged as a promising tool to study minor radiation-induced defects in semiconductor materials. In this study, we propose a sensitive non-destructive technique for investigating radiation-induced defects using multivariate statistical analyses combined with Raman spectroscopy. Raman spectroscopy is a contactless and non-destructive method widely used to characterize semiconductor materials and their defects. The multivariate statistical methods applied in analyzing the Raman spectra provide high sensitivity in detecting minor radiation-induced defects. The proposed technique was demonstrated by categorizing 100–500 kGy irradiated GaAs wafers into samples with low and high irradiation levels using linear discrimination analysis ML algorithms. Despite the high similarity in the obtained Raman spectra, the ML algorithms correctly predicted the blind testing samples, highlighting the effectiveness of ML in defect study. This study provides a promising approach for detecting minor radiation-induced defects in semiconductor materials and can be extended to other semiconductor materials and devices.

Comparative analysis of machine learning algorithms for biomedical text document classification: A case study on cancer-related publications

Biomedical text document classification is an essential task within Natural Language Processing (NLP), with applications ranging from sentiment analysis to authorship identification. Despite advancements in traditional machine-learning algorithms like Support Vector Machines (SVM) and Logistic Regression, challenges such as data sparsity and high dimensionality persist. Recent years have seen a surge in the use of deep learning models to mitigate these issues. This study aims to conduct a comparative analysis of various machine-learning algorithms for classifying biomedical text documents. The study employs the "Medical Text Dataset - Cancer Doc Classification" from Kaggle, comprising 7570 biomedical text documents labeled into three types of cancer (colon, lung, and thyroid). A preprocessing pipeline involving tokenization, stop-word removal, and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization is applied. Algorithms including Logistic Regression, SVM, and Multinomial Naive Bayes are evaluated through 5-fold cross-validation. Performance metrics like accuracy, precision, recall, F1 score, and area under the ROC curve (AUC ROC) are employed. Logistic Regression outperforms the other algorithms with an accuracy of 78.3% and an AUC ROC of 88.59%. SVM and Multinomial Naive Bayes follow with lower performance metrics. Hyperparameter tuning further enhances the performance of the algorithms, particularly Logistic Regression. The study makes a significant contribution to the field of biomedical text classification by systematically comparing machine-learning algorithms. Logistic Regression emerges as the most effective, emphasizing the importance of algorithm selection and hyperparameter tuning in machine learning applications within this domain.

Comparative Analysis of Machine Learning Algorithms in Stroke Prediction

Comparative Analysis of Machine Learning Algorithms in Stroke Prediction

Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Feature Selection based Performance Analysis of Machine Learning Algorithms in Network Intrusion Detection

Information and data security is one of the most challenging tasks for the massive-scale digital revolution all over the world. The very first step to secure our data is to identify invasive conduct and intrusive behavior. However, due to the high scalability of most modern systems and the complex nature of the attacks, the traditional detection system is less reliable. To overcome this challenge, it is necessary to build intelligent and adaptive intrusion detection technologies. That is why this research developed an intrusion detection system using commonly used ML algorithms and analyzed performance from different perspectives. In our pipeline, this exploration applied both supervised and unsupervised learning algorithms. The training and test data were split in multiple ways to evaluate the performance of the models. From the experimental results, it was found that the Light Gradient Boosting Machine (LightGBM) performs better in our context in terms of both precision and recall.

Comparative Analysis of Machine Learning Algorithms With Advanced Feature Extraction for ECG Signal Classification

Comparative Analysis of Machine Learning Algorithms With Advanced Feature Extraction for ECG Signal Classification

A Comparative Analysis of Machine Learning Algorithms for Aggregated Electric Chargepoint Load Forecasting

A Comparative Analysis of Machine Learning Algorithms for Aggregated Electric Chargepoint Load Forecasting

Critical analysis of hybrid learning models to detect morphed images

Machine learning (ML) algorithms produce different results with the kind of input data set or parameters passed to the algorithm. As per input data set, the analysis generally depends on the data type and amount of data set being used to train and test. Other parameters which affect the machine learning analysis are the CNN parameters or the hybrid approach being used. As there is no similar result for ML analysis on various data set, it is hard to predict which model will work most efficiently on the given data set. In this paper an analytical analysis of machine learning algorithms has been done to examine the working of various algorithms individually or in Hybrid mode. This paper studies CNN, CNN + RF, CNN +SVM approach and the theoretical parameters affecting them which helps in deciding the best suited algorithm with the given data set.

An Integrated Business Intelligence Framework for Sustainable Risk Mitigation

This research paper examines the critical juncture of business intelligence and sustainable risk management in response to the increasing challenges faced by modern businesses. Our study recognizes that organizations must navigate uncertainties while prioritizing sustainability. It focuses on analyzing credit risk data. We present a comprehensive examination of predictive performance using Logistic Regression, Decision Tree, and K-Nearest Neighbors classifiers augmented by the Synthetic Minority Over-sampling Technique (SMOTE) for class rebalancing. The empirical findings presented through detailed tables and figures reveal intricate relationships and patterns within the data. This research also contributes to the broader discourse on responsible business practices by highlighting the integration of business intelligence in sustainable risk mitigation. Moreover, comparative analysis of machine learning algorithms under various resampling techniques further strengthens the framework’s reliability.

Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

Analysis of Machine Learning Algorithms for Intrusion Detection in Wireless Sensor Networks

Analysis of Machine Learning Algorithms for Intrusion Detection in Wireless Sensor Networks

Using Machine Learning to Predict Heart Failure: A Comparative Analysis of Various Classification Algorithms

This study presents a comprehensive analysis of machine learning algorithms for predicting heart failure, a significant cause of morbidity and mortality worldwide. Employing a robust dataset of patient records with diverse clinical features, the performance of several widely-used classification algorithms, including K-Nearest Neighbours (KNN), Decision Trees, Support Vector Machines (SVM), Random Forests (RF), and Gaussian Naive Bayes etc. are systematically evaluated and compared. The methodology encompasses loading the dataset, data pre-processing, feature selection, model training, and validation. The performance of each algorithm is rigorously assessed using metrics such as accuracy, precision, recall, F1-score, and the area under the ROC curve with Random Forest outperforming the other classification algorithms. The study further investigates the influence of hyper-parameter tuning and cross-validation on model efficacy. Additionally, an interpretative analysis of the models, offering insights into feature importance and the clinical relevance of the prediction outcomes were provided. The findings aim to contribute to the field of medical informatics by identifying the most effective machine learning strategies for heart failure prediction, thereby facilitating early intervention and personalized patient care. This research not only underscores the potential of machine learning in healthcare but also highlights the challenges and considerations in developing reliable predictive models for complex medical conditions.

A comparative analysis of machine learning algorithms for predicting wave runup

The present study uses nine machine learning (ML) methods to predict wave runup in an innovative and comprehensive methodology. Unlike previous investigations, which often limited the factors considered when applying ML methodologies to predict wave runup, this approach takes a holistic perspective. The analysis takes into account a comprehensive range of crucial coastal parameters, including the 2% exceedance value for runup, setup, total swash excursion, incident swash, infragravity swash, significant wave height, peak wave period, foreshore beach slope, and median sediment size. Model performance, interpretability, and practicality were assessed. The findings from this study showes that linear models, while valuable in many applications, proved insufficient in grasping the complexity of this dataset. On the other hand, we found that non-linear models are essential for achieving accurate wave runup predictions, underscoring their significance in the context of the research. Within the framework of this examination, it was found that wave runup is affected by median sediment size, significant wave height, and foreshore beach slope. Coastal engineers and managers can utilize these findings to design more resilient coastal structures and evaluate the risks posed by coastal hazards. To improve forecast accuracy, the research stressed feature selection and model complexity management. This research proves machine learning algorithms can predict wave runup, aiding coastal engineering and management. These models help build coastal infrastructure and predict coastal hazards.Graphical

Comparative analysis of machine learning algorithms used in malware analysis

Despite the achievement of the field of cyber security in the modern world of science and the continuous development of its methods, malware is still one of the biggest threats to information security. Malware is evolving every day, and its types and behaviors are increasing day by day. And the importance of using modern, sophisticated technologies in identifying and combating such complex and diverse malicious programs is increasing. In this regard, it is possible to mention the advantages of using intelligent systems in the field of information security. In this article, we will analyze PE (Portable Executable) files on the Windows operating system, that is, the processes running on the computer and analyzing the malicious programs using machine learning algorithms. At the same time, we will focus on the operation of different machine learning algorithms and show which method is most effective to use for our example. In this article, we will have the following tasks: Providing information about malicious software. Definition of PE files, its structure and nature. Preparation of data for practical work (collection of files with clean and malicious code). An overview of methods for separating files into clean and malicious files. Sorting the signs necessary for training according to pre-prepared files, that is, getting only those signs that allow to achieve the most accurate result during training. Implementation of several machine learning algorithms and selection of the most effective among them.