Analysis Of Machine Learning Algorithms Research Articles

A Comparative Analysis of Machine Learning Algorithms in Traditional and Cloud Computing Environments Using Medical Data

A Comparative Analysis of Machine Learning Algorithms in Traditional and Cloud Computing Environments Using Medical Data

Abstract 5397: Prediction of conversion surgery completion following neoadjuvant modified FOLFIRINOX in borderline resectable and locally advanced pancreatic adenocarcinoma: machine learning algorithm analysis

Abstract Background: Neoadjuvant modified (m) FOLFIRINOX is a standard therapy for medically fit patients with borderline resectable pancreatic cancer (BRPC) and locally advanced unresectable pancreatic cancer (LAPC). Completion of conversion surgery following mFOLFIRINOX is an important prognostic factor. In this study, we evaluated multiple machine learning models to predict completion of conversion surgery following neoadjuvant mFOLFIRINOX in BRPC and LAPC. Methods: Between January 2017 and December 2020, a total of 647 patients with BRPC and LAPC treated with neoadjuvant mFOLFIRINOX at Asan Medical Center, Seoul, South Korea were enrolled. All demographic and clinicopathological data were retrospectively collected; age, ECOG performance status, BMI, resectability as per the NCCN guidelines, tumor location, tumor size, maximum standardized uptake value assessed by 18F-FDG PET/CT, number of cycles of mFOLFIRINOX, tumor response to mFOLFIRINOX, and serum CA 19-9. Several machine learning models, such as logistic regression and random forest classifier, were trained to predict curative-intent resection and were then internally validated. The relative importance of each variable was analyzed using both Gini importance and permutation importance. Results: Among 647 patients, 173 (26.7%) underwent curative-intent conversion surgery (R0 or R1) following mFOLFIRINOX. The patients who underwent surgery showed significantly better overall survival (median 42.1 vs 17.2 months, P<0.0001) than those who did not. In multivariable analysis using logistic regression, age, resectability as per the NCCN guidelines, tumor size, number of cycles of mFOLFIRINOX, tumor response to mFOLFIRINOX, and CA 19-9 change rate (percentage change in CA 19-9 after mFOLFIRINOX) were significantly correlated to tumor removal with curative-intent conversion surgery. Random forest classifier showed the best predictive capability with AUC at 0.832. The most important feature was the CA 19-9 change rate. Conclusions: In this large cohort-based analysis, completion of conversion surgery could be predicted successfully in BRPC and LAPC patients treated with neoadjuvant mFOLFIRINOX using machine learning models. Further external validation is necessary for generalization. Citation Format: Hyunseok Yoon, Kyu-Pyo Kim, Inkeun Park, Jae Ho Jeong, Heung-Moon Chang, Baek-Yeol Ryoo, Changhoon Yoo. Prediction of conversion surgery completion following neoadjuvant modified FOLFIRINOX in borderline resectable and locally advanced pancreatic adenocarcinoma: machine learning algorithm analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5397.

The Performance Analysis of Machine Learning Algorithms for Credit Card Fraud Detection

This paper studies the performance analysis of machine learning (ML) and data mining techniques for anomaly detection in credit cards. As the usage of digital money or plastic money grows in developing nations, so does the risk of fraud. To counter these scams, we need a sophisticated fraud detection method that not only identifies the fraud but also detects it before it occurs efficiently. We have introduced the notion of credit card fraud and its many variants in this research. Numerous ML fraud detection approaches are studied in this paper including Principal Component Analysis (PCA) data mining and the Fuzzy C-Means methodologies, as well as the Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB) algorithms. The existing and proposed models for credit card fraud detection have been thoroughly reviewed, and these strategies have been compared using quantitative metrics including accuracy rate and characteristics curves. This paper discusses the shortcomings of existing models and proposes an efficient technique to analyze the fraud detection.

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms.

Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings. Graphical abstract.

Analysis of Machine Learning Algorithms in Predicting the Flood Status of Jakarta City

By mining the information in the dataset, we can solve a prediction problem, especially flood status prediction based on floodgate levels, using machine learning algorithms. This research employs three machine learning algorithms (K-Nearest Neighbor, Naive Bayes, and Support Vector Machine) for predicting the flood status using a dataset containing the data of DKI Jakarta's floodgate levels. Using a 5-fold, 10-fold, and 20-fold cross-validation evaluation, we get the highest accuracy (85.096%), f-score (85.1%), precision (85.641%), and recall (85.096%) from the model using the SVM algorithm with a polynomial kernel. Average performance-wise, the K-NN algorithm performs better than the other algorithm with an average accuracy of 83.147%, an average f-score of 83.156%, an average precision of 83.566%, and an average recall of 83.147%.

Accuracy Assessment of Machine Learning Algorithms Used to Predict Breast Cancer

Machine learning (ML) was used to develop classification models to predict individual tumor patients’ outcomes. Binary classification defined whether the tumor was malignant or benign. This paper presents a comparative analysis of machine learning algorithms used for breast cancer prediction. This study used a dataset obtained from the National Cancer Institute (NIH), USA, which contains 1.7 million data records. Classical and deep learning methods were included in the accuracy assessment. Classical decision tree (DT), linear discriminant (LD), logistic regression (LR), support vector machine (SVM), and ensemble techniques (ET) algorithms were used. Probabilistic neural network (PNN), deep neural network (DNN), and recurrent neural network (RNN) methods were used for comparison. Feature selection and its effect on accuracy were also investigated. The results showed that decision trees and ensemble techniques outperformed the other techniques, as they both achieved a 98.7% accuracy.

Genome-wide DNA methylation and transcription analysis in psoriatic epidermis.

Aim: To identify DNA methylation and transcription biomarkers in the psoriatic epidermis. Materials & methods: Gene transcription and DNA methylation datasets of psoriatic epidermal tissue were obtained from the Gene Expression Omnibus. Machine learning algorithm analysis and weighted gene coexpression network analysis were carried out to screen hub genes. Results: Differentially methylated and expressed genes were identified in the psoriatic epidermis. Six hub genes were selected - GZMB, CRIP1, S100A12, ISG15, CRABP2 and VNN1- whose transcript levels showed a significant correlation with Psoriasis Area and Severity Index scores and immune infiltration. Conclusion: Psoriatic epidermis is primarily in a hypermethylated status. Epidermis-specific hub differentially methylated and expressed genes are potential biomarkers to help judge the condition of psoriasis.

Comparative Analysis of Machine Learning Algorithms for Early Prediction of Parkinson’s Disorder based on Voice Features

Parkinson Disorder (PD) is a neurological disorder which is progressive in nature and has no cure. Early diagnosis of PD plays a key role in delaying the progression of the disorder. Dysphonia is the most prominent early symptom which is exhibited by approximately 90% of PD patients. Voice features based early diagnosis with the integration of Artificial Intelligence plays a prominent role in providing accurate, non-invasive, and robust predictions to PD patients. This paper focuses on providing comparative and experimental analysis of Machine Learning (ML) algorithms for the prediction of PD based on the voice features dataset retrieved from the UCI repository. This paper presents the results from the four sampling experiments conducted with different traditional ML algorithms for the retrieved voice dataset. The results of this study make it evident that Naïve Bayes provides a highest accuracy of 89% when compared to other ML algorithms. This study helps in identifying the best ML algorithm among the traditional ML algorithms for PD prediction based on voice features dataset.

Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data.

Machine learning has the potential to enhance damage detection and prediction in materials science. Machine learning also has the ability to produce highly reliable and accurate representations, which can improve the detection and prediction of damage compared to the traditional knowledge-based approaches. These approaches can be used for a wide range of applications, including material design; predicting material properties; identifying hidden relationships; and classifying microstructures, defects, and damage. However, researchers must carefully consider the appropriateness of various machine learning algorithms, based on the available data, material being studied, and desired knowledge outcomes. In addition, the interpretability of certain machine learning models can be a limitation in materials science, as it may be difficult to understand the reasoning behind predictions. This paper aims to make novel contributions to the field of material engineering by analyzing the compatibility of dynamic response data from various material structures with prominent machine learning approaches. The purpose of this is to help researchers choose models that are both effective and understandable, while also enhancing their understanding of the model's predictions. To achieve this, this paper analyzed the requirements and characteristics of commonly used machine learning algorithms for crack propagation in materials. This analysis assisted the authors in selecting machine learning algorithms (K nearest neighbor, Ridge, and Lasso regression) to evaluate the dynamic response of aluminum and ABS materials, using experimental data from previous studies to train the models. The results showed that natural frequency was the most significant predictor for ABS material, while temperature, natural frequency, and amplitude were the most important predictors for aluminum. Crack location along samples had no significant impact on either material. Future work could involve applying the discussed techniques to a wider range of materials under dynamic loading conditions.

Comparative Analysis of Machine Learning Algorithms for Soil Erosion Modelling Based on Remotely Sensed Data

Recent years have seen an increase in the use of remote-sensing based methods to assess soil erosion, mainly due to the availability of freely accessible satellite data, with successful results on a consistent basis. There would be valuable benefits from applying these techniques to the Arctic areas, where ground local studies are typically difficult to perform due to hardly accessible roads and lands. At the same time, however, the application of remote-sensing methods comes with its own set of challenges when it comes to the peculiar features of the Arctic: short growing periods, winter storms, wind, and frequent cloud and snow cover. In this study we perform a comparative analysis of three commonly used classification algorithms: Support Vector Machine (SVM), Random Forest (RF) and Multilayer Perceptron (MLP), in combination with ground truth samples from regions all over Iceland, provided by Iceland’s Soil Conservation Service department. The process can be automated to predict soil erosion risk for larger, less accessible areas from Sentinel-2 images. The analysis performed on validation data sets supports the effectiveness of both approaches for modeling soil erosion, albeit differences are highlighted.

Comparative Performance Analysis of Machine Learning Algorithms for Arm and Shoulder Exercises Using Wrist-Worn Band

Comparative Performance Analysis of Machine Learning Algorithms for Arm and Shoulder Exercises Using Wrist-Worn Band

A comparative analysis of machine learning algorithms for fake news detection

A comparative analysis of machine learning algorithms for fake news detection

Comparative Analysis of Machine Learning Algorithms to Forecast Indian Stock Market

Forecasting the stock market is a complex and challenging task, as it involves analyzing a vast amount of data and taking into account various economic, political, and social factors. This paper presents an overview of different approaches and techniques used for stock market forecasting, including fundamental analysis and machine learning. The study also highlights the different algorithms used and discusses their effectiveness in predicting the stock market. This research proposes to use five different algorithms as Decision Trees, Random forest, Generalized Linear model,Gradient boosted trees, and Support Vector Machines. This research identifies models that are close to real predictions. These algorithms are applied to BSE index data from November 2017 to February28, 2023.

A Comparative Analysis of Machine Learning Algorithms on Card-Based Financial Fraud Detection with Infusion of Sigmoid and Isotonic Functions

There is no doubt that the commencement of the e-revolution in the financial sector of the economy has introduced opportunity for electronic fraud in the card payment ecosystem globally. This is occasioned by factors such as increased knowledge in the fintech space, poverty, pair pressure on the perpetrators. This study was focused on comparing of several known Machine leaning Algorithms – Logistic regression, Decision trees, Random Forest, Extra-Trees, Adaboost and Gradient boosting on how they perform comparatively when applied to Fraud detection. The raw data used were obtained from The Xente Fraud Detection data set used for the development of the financial fraud detection model which includes sample of approximately 140,000 transactions categorized into Fraud and Non-Fraud. Results from the study indicated that Adaboost Model outperformed the remaining applied models thereby making Adaboost a good model for fraud detection. Further research work can be carried out by comparing the performance with other Deep Learning Algorithms.

Comparative analysis of machine learning algorithms for predicting standard time in a manufacturing environment

Abstract Determining accurate standard time using direct measurement techniques is especially challenging in companies that do not have a proper environment for time measurement studies or that manufacture items requiring complex production schedules. New and specific time measurement techniques are required for such companies. This research developed a novel time estimation approach based on several machine learning methods. The set of collected inputs in the manufacturing environment, including a number of products, the number of welding operations, product's surface area factor, difficulty/working environment factors, and the number of metal forming processes. The data were collected from one of the largest bus manufacturing companies in Turkey. Experimental results demonstrate that when model accuracy was measured using performance measures, k-nearest neighbors outperformed other machine learning techniques in terms of prediction accuracy. “The number of welding operations” and “the number of pieces” were found to be the most effective parameters. The findings show that machine learning algorithms can estimate standard time, and the findings can be used for several purposes, including lowering production costs, increasing productivity, and ensuring efficiency in the execution of their operating processes by other companies that manufacture similar products.

Forecasting Foreign Direct Investment to Sub-Saharan Africa using Arima Model: A Comparative Analysis of Machine Learning Algorithms

Forecasting Foreign Direct Investment to Sub-Saharan Africa using Arima Model: A Comparative Analysis of Machine Learning Algorithms

Analysis of Machine learning algorithms for predicting a student's grade

Analysis of Machine learning algorithms for predicting a student's grade

Convergence analysis of machine learning algorithms for the numerical solution of mean field control and games: II—the finite horizon case

We propose two numerical methods for the optimal control of McKean–Vlasov dynamics in finite time horizon. Both methods are based on the introduction of a suitable loss function defined over the parameters of a neural network. This allows the use of machine learning tools, and efficient implementations of stochastic gradient descent in order to perform the optimization. In the first method, the loss function stems directly from the optimal control problem. The second method tackles a generic forward-backward stochastic differential equation system (FBSDE) of McKean–Vlasov type, and relies on suitable reformulation as a mean field control problem. To provide a guarantee on how our numerical schemes approximate the solution of the original mean field control problem, we introduce a new optimization problem, directly amenable to numerical computation, and for which we rigorously provide an error rate. Several numerical examples are provided. Both methods can easily be applied to certain problems with common noise, which is not the case with the existing technology. Furthermore, although the first approach is designed for mean field control problems, the second is more general and can also be applied to the FBSDEs arising in the theory of mean field games.

Analysis of Machine Learning Algorithm for Sleep Apnea Detection Based on Heart Rate Variability

Sleep apnea is a common problem with health implications ranging from excessive daytime sleepiness to serious cardiovascular disorders. The method for detecting and measuring sleep apnea is through breathing monitoring (polysomnography), which is time consuming and relatively expensive. Cardiovascular which is closely related to heart performance activities allows the use of electrocardiogram (heart rate variability) features to detect sleep apnea. This study aims to compare the results of sleep apnea detection using several machine learning algorithms. A total of 2,445 data were divided into 1,834 data as learning sets and 611 data as test sets. Evaluation of 10-fold cross-validation using all HRV features shows that neural network algorithm has the best performance compared to decision tree algorithm, k-nearest neighbor, and support vector machine with an accuracy rate (82.44% in the learning set, 79.21% in the test set consecutively), precision (85.54% and 82.70%), f-measure (87.70% and 85.67%), and AUC (0.867 and 0.832). Based on the results of performance testing using only selected HRV features (CVRR, HF, SD1/SD2 Ratio, and S-Region), the K-Nearest Neighbors, Support Vector Machine, and Neural Network algorithms experienced a decrease in performance. The use of all HRV features is recommended compared to only using selected HRV features, so it can help detect the presence/absence of sleep apnea much better.

An Empirical Analysis of Machine Learning Algorithms for Solar Power Forecasting in a High Dimensional Uncertain Environment

In the fabric of energy generation, solar power is the most promising clean energy solution as an alternative to non-renewable energy sources. However, solar power’s dependency on environmental factors adds uncertainty to energy production. In such a scenario, solar power forecasting provides an edge to mitigate this uncertainty and improves overall system stability. Recently, machine learning (ML) models have been extensively deployed for designing and forecasting solar power. However, data pre-processing, forecast horizon, and performance evaluation of ML algorithms have to be carefully evaluated to find an accurate model. This paper provides an empirical comparison of different generation ML models for solar power forecasting, which can help understand future research on which method to adopt, depending on the ML model’s strengths and weaknesses. Therefore, an effective forecasting method is designated in aspects such as performance errors, convergence time, and computational complexity. So, this work rates different ML models on error performance metrics and convergence time. Moreover, cross-fold validations and hyperparameter are also examined for the top five performing models for a comprehensive evaluation and to give more intuitive and calibrated insight into various stakeholders working in the solar power plant modeling field.