Analysis Of Machine Learning Algorithms Research Articles

Performance analysis of machine learning algorithms: Single Model VS Ensemble Model

Machine Learning is a branch of Artificial Intelligence that predicts several naturally occurring events by training a model with some data and then using unseen data to test it. This paper seeks to analyze the performances of single and ensemble machine learning algorithms on the Cleveland Heart disease data set. Experimental study proves that the accuracy score and area under the ROC curve in the ensemble machine learning model is higher than the single machine learning model in predicting non-CVD and CVD patients.

A Systematic Review on Machine Learning Algorithms for Customer Satisfaction Classification in Various Fields

Purpose: A variety of soft approaches have already been considered in relation to the growth of marketing. Problems related to customer satisfaction and retention have been studied earlier. The application of business intelligence, artificial intelligence, and data mining has a great impact on the development of business organizations. Customers are increasing day by day in every field, which is complicating the method of finding their satisfaction level. To find the best method to solve these complexities, advanced machine learning approaches can be used. This paper discusses a detailed literature survey of various approaches used to identify customer satisfaction and retention using customer logs in various fields. Design/Methodology/Approach: The details collected for this review paper were obtained by analysing and comparing different research articles from recognized resources. Objective: To find a research gap and appropriate solutions for customer satisfaction accuracy using machine learning approaches. Results/ Findings: Review of this paper gives a proper understanding of customer satisfaction in various domains using machine-learning approaches Originality/Value: The review of this paper gives an analysis of machine learning algorithms for customer satisfaction in various fields and suggests the importance of new classification models. Type of Paper: Literature Review.

Comparative analysis of machine learning algorithms on prediction of the sound absorption coefficient for reconfigurable acoustic meta-absorbers

Exploring the potential of sound absorption is foremost for improving room acoustics and unleashing the power of noise remediation. Acoustic metamaterials are considered promising candidates for tunable broadband sound absorption in the low-to-high frequency range. In this work, an acoustic meta-absorber with multi-order Helmholtz resonator cavities is proposed. The meta-absorber unit cell comprises multiple disks, spacers, and a bottom cavity arranged in a series configuration. Therefore, the assembled acoustic meta-absorber is reconfigurable to have different absorption characteristics. Experimental results demonstrate that the proposed meta-absorber unit cell exhibits high sound absorption, with multiple absorption peaks observed within the 80-1600 Hz frequency range. Additionally, tunable broadband and quasi-perfect absorption (≥0.9) can be achieved at low frequencies (≤600 Hz) by adjusting the positions of individual disks and other structural parameters. Furthermore, a feedforward network-design approach based on machine learning models is proposed and experimentally demonstrated for the meta-absorber. Two types of meta-absorber features and different machine learning algorithms, including artificial neural network (ANN), k-Nearest Neighbor (kNN), and radial basis function neural network (RBFN), are utilized to compare their predictive abilities. The implementation of machine learning models allows for efficient design optimization of the meta-absorber's geometrical parameters to achieve specific functionalities without relying on physical models. The geometrical parameters are adaptively tuned during the design process to enhance sound absorption performance, particularly at low frequencies. The predicted sound absorption values are compared with the experimental results, and among the machine learning algorithms employed, ANN demonstrates the best performance, followed by the kNN method.

Keystroke Biometric Data for Identity Verification: Performance Analysis of Machine Learning Algorithms

Cyber-attacks are on the rise in today's environment, where traditional security measures are ineffective. As a result, the adoption of cutting-edge tools such as artificial intelligence technology is critical in the fight against cyber threats. User behaviors, such as keyboard dynamics, provide potential data that can be used for protection against cyber-attacks. Keystroke dynamics is one of the fastest and most cost-effective methods that can be used to detect user behaviors, as it can be captured using standard user keyboards. The analysis of this data and protection against cyber-attacks is made possible through machine learning algorithms. Based on keyboard dynamics, this study analyzes the performance of k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Random Forest (RF), and Neural Network (NN) methods for user behavior analysis and anomaly detection. The findings shed light on the significance of artificial intelligence in cyber security by examining the accomplishments of several machine learning algorithms. The study's findings may serve as a foundation for future research and novel solutions in the realm of cyber security.

A comprehensive comparison and analysis of machine learning algorithms including evaluation optimized for geographic location prediction based on Twitter tweets datasets

A comprehensive comparison and analysis of machine learning algorithms including evaluation optimized for geographic location prediction based on Twitter tweets datasets

Survey and comparative analysis of machine learning algorithms for breast cancer diagnosis: A comprehensive review

Breast cancer is a significant health concern worldwide, and early and accurate diagnosis plays a crucial role in improving patient outcomes. Machine learning algorithms have emerged as powerful tools for analyzing complex medical data and aiding in the diagnosis of breast cancer. This paper provides an overview of the application of machine learning algorithms in breast cancer diagnosis. The findings indicate that machine learning algorithms, such as support vector machines (SVM), random forests, artificial neural networks (ANN), and deep learning models, have been extensively explored for breast cancer diagnosis. These algorithms leverage the vast amounts of available data, including patient demographics, medical history, imaging data (mammography, ultrasound, MRI), and genetic profiles, to identify patterns and make predictions. One of the primary applications of machine learning algorithms in breast cancer diagnosis is the classification of tumors as malignant or benign. By training on labeled datasets, these algorithms can learn to differentiate between cancerous and non-cancerous cases, thus assisting in accurate tumor diagnosis. Additionally, machine learning algorithms can be used to predict the likelihood of cancer recurrence, which helps guide treatment decisions and post-treatment monitoring. Feature selection and extraction techniques also play a vital role in breast cancer diagnosis using machine learning algorithms. These techniques aim to identify the most relevant features or biomarkers associated with breast cancer, reducing the dimensionality of the data and enhancing the performance of the models. Feature selection algorithms, such as recursive feature elimination and correlation-based feature selection, contribute to the identification of critical indicators for accurate diagnosis. Furthermore, the integration of different data sources and modalities, such as combining clinical data with imaging data or genetic data, has shown promise in improving breast cancer diagnosis accuracy. By fusing multiple types of information, machine learning algorithms can leverage the complementary nature of these data sources to enhance diagnostic capabilities. Despite the advancements made, challenges remain in the field of breast cancer diagnosis using machine learning algorithms. Issues such as data quality, interpretability of models, and generalizability to diverse populations need to be addressed to ensure the reliable and equitable application of these algorithms in clinical practice.

Comparative Analysis of Machine Learning Algorithms in Breast Cancer Classification

Comparative Analysis of Machine Learning Algorithms in Breast Cancer Classification

Disease-Specific haptoglobin N-Glycosylation in inflammatory disorders between cancers and benign diseases of 3 types of female internal genital organs

BackgroundN-glycosylation of the haptoglobin is closely related to pathological states. This study aims to evaluate the association of glycosylation of disease-specific Hp (DSHp) β chain with different pathological states of the cervix, uterus, and ovary to explore differences in their inflammatory responses and to screen potential biomarkers to distinguish cancer from benign diseases. MethodsDSHp-β chains of 1956 patients with cancers and benign diseases located in the cervix, uterus, and ovary organs were separated from serum immunoinflammatory-related protein complexes (IIRPCs). The N-glycopeptides from DSHp-β chains were detected using mass spectrometry, followed by an analysis of machine learning algorithms. Results55 N-glycopeptides at N207/N211, 19 at N241, and 21 at N184 glycosylation sites of DSHp for each sample were identified. Fucosylation and sialylation of DSHp in cervix, uterus, and ovary cancer were significantly increased compared to their corresponding benign diseases (p < 0.001). The cervix diagnostic model, a combination of G2N3F, G4NFS, G7N2F2S5, GS-N&GS-N, G2N2&G4N3FS, G7N2F2S5, G2S2&G-N, and GN2F&G2F at N207/N211 sites, G3NFS2 and G3NFS at N241site, G9N2S, G6N3F6, G4N3F5S, G4N3F4S2, and G6N3F4S at N184 site), has shown a good diagnostic capability to distinguish cancer from benign diseases, with the area under curve (AUC) of 0.912. The uterus diagnostic model including G4NFS, G2S2&G2S2, G3N2S2, GG5N2F5, G2&G3NFS, and G5N2F3S3 at N207/N211 sites, and G2NF3S2 at N184 site, with an AUC of 0.731. The ovary diagnostic model including G2N3F, GF2S-N &G2F3S2, G2S&G2, and G2S&G3NS at N207/N211 sites; G2S and G3NFS at N241 site, G6N3F4S at N184 site, with an AUC of 0.747. ConclusionsThese findings provide insights into differences in organ-specific inflammatory responses of DSHp for different pathological states among the organs of the cervix, uterus, and ovary.

Application and Analysis of Machine Learning Algorithms on Pima and Early Diabetes Datasets for Diabetes Prediction

Diabetes is a chronic condition that strike how your body burns food for energy. Much of the food you consume is converted by your body into sugar (glucose), which is then released into your bloodstream. Your pancreas releases insulin when your blood sugar levels rise. Over the years, several scholars have sought to create reliable diabetes prediction models. Due to a lack of adequate data sets and prediction techniques, this discipline still faces many unsolved research issues, which forces researchers to apply big data analytics and ML-based methodology. Four distinct machine learning algorithms are used in the study to analyze healthcare prediction analytics and solve the issues. In this investigation, the Pima and Early detection datasets were employed. We applied the Decision Tree, MLP, Naive Bayes, and Random Forest algorithms to these datasets and evaluated the accuracy and F-Measure. The goal of this research is to develop a system that could more precisely predict a patient's risk of developing diabetes.

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&amp;lt;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