Comparative Analysis Of Classification Algorithms Research Articles

Detecting health misinformation: A comparative analysis of machine learning and graph convolutional networks in classification tasks

In the digital age, the proliferation of health-related information online has heightened the risk of misinformation, posing substantial threats to public well-being. This research conducts a meticulous comparative analysis of classification models, focusing on detecting health misinformation. The study evaluates the performance of traditional machine learning models and advanced graph convolutional networks (GCN) across critical algorithmic metrics. The results comprehensively understand each algorithm's effectiveness in identifying health misinformation and provide valuable insights for combating the pervasive spread of false health information in the digital landscape. GCN with TF-IDF gives the best result, as shown in the result section.•The research method involves a comparative analysis of classification algorithms to detect health misinformation, exploring traditional machine learning models and graph convolutional networks.•This research used algorithms such as Passive Aggressive Classifier, Random Forest, Decision Tree, Logistic Regression, Light GBM, GCN, GCN with BERT, GCN with TF-IDF, and GCN with Word2Vec were employed. Performance Metrics: Accuracy: for Passive Aggressive Classifier: 85.75 %, Random Forest: 86 %, Decision Tree: 81.30 %, Light BGM: 83.29 %, normal GCN: 84.53 %, GCN with BERT: 85.00 %, GCN with TR-IDF: 93.86 % and GCN with word2Vec: 81.00 %•Algorithmic performance metrics, including accuracy, precision, recall, and F1-score, were systematically evaluated to assess the efficacy of each model in detecting health misinformation, focusing on understanding the strengths and limitations of different approaches. The superior performance of Graph Convolutional Networks (GCNs) with TF-IDF embedding, achieving an accuracy of 93.86 %

Comparative Analysis of Classification Algorithms for Heart Disease

Abstract: Heart is the main component of the human body and without it the body can’t function. It provides the flow of blood of different organs and body parts. It purifies the blood by removing the carbon dioxide(co2). It is also known as cardiovascular disease, it creates many risk factors for a human, including death. Heart disease is one of the most common causes of death around the world nowadays. Often, the enormous amount of information is gathered to detect diseases in medical science. All of the information is not useful but vital in taking the correct decision. Thus, it is not always easy to detect the heart disease because it required skilled knowledge or experiences about heart failures symptoms for an early prediction. Most of the medical dataset are dispersed, widespread and assorted. However data mining is a robust technique for extracting invisible, predictive and actionable information from the extensive databases. for identifying the possibility of heart disease in a patient. This work is justified by performing a comparative study and analysis using classification algorithms namely Logistic Regression, KNN, SVM, Decision Tree, and Random Forest are used at different levels of evaluation. Further, this research work is aimed towards identifying the best classification algorithm for identifying the possibility of heart disease in a patient. This work is justified by performing a comparative study and analysis using three classification algorithms namely Logistic Regression, KNN, SVM, Decision Tree and Random Forest are used at different levels of evaluation.

A Comparative Analysis of Classification Algorithms for Cyberbullying Crime Detection: An Experimental Study of Twitter Social Media in Indonesia

Purpose: This research aims to identify content that contains cyberbullying on Twitter. We also conducted a comparative study of several classification algorithms, namely NB, DT, LR, and SVM. The dataset we use comes from Twitter data which is then manually labeled and validated by language experts. This study used 1065 data with a label distribution, namely 638 data with a non-bullying label and 427 with a bullying label.Methods: The weighting process for each word uses the bag of word (BOW) method, which uses three weighting features. The three-word vector weighting features used include unigram, bigram, and trigram. The experiment was conducted with two scenarios, namely testing to find the best accuracy value with the three features. The following scenario looks at the overall comparison of the algorithm's performance against all the features used.Result: The experimental results show that for the measurement of accuracy weighting based on features and algorithms, the SVM classification algorithm outperformed other algorithms with a percentage of 76%. Then for the weighting based on the average recall, the DT classification algorithm outperformed the other algorithms by an average of 76%. Another test for measuring overall performance (F-measure) based on accuracy and precision, the SVM classification algorithm, managed to outperform other algorithms with an F-measure of 82%.Value: Based on several experiments conducted, the SVM classification algorithm can detect words containing cyberbullying on social media.

Comparative analysis of classification algorithms on the breast cancer recurrence using machine learning

This paper presents a comparative evaluation of classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) software. The main goal of the paper is to conduct a comprehensive comparison and determine which predictive modelling technique is best for the problem of classifying breast cancer recurrence. The dataset for this study consists of 286 instances (201 instances belong to recurrence class and 85 instances belong to non-recurrence class) and 10 attributes. Comparison analysis is conducted for Naïve Bayes, J48, K*, Random Forest, Multilayer Perceptron (MLP) and Support Vector Machine (SVM) models using different parameters. The performance of the developed models is calculated using the following evaluation metrics: accuracy, precision, sensitivity, specificity, mean absolute error, ROC curves and AUC values. Contribution of the attributes to the classification models is assessed by measuring information gain. Results show that J48 model and the SVM algorithm give the highest accuracy, which is 75.5% and 79.6%, respectively. Implementation of SVM algorithm also shows the highest sensitivity of 99%, while the highest precision is obtained by MLP algorithm which is 79%. In addition, SVM algorithm possesses the lowest mean absolute error. Furthermore, by measuring information gain, it is revealed that a degree of malignant tumour contributes more than other attributes to recurrence of breast cancer.

A comparative analysis of classification algorithms for consumer credits

Abstract Machine Learning is a constantly growing area which has the capacity to analyze massive amounts of data and find relevant patterns, a very important feature in the era of big data. It has a wide range of application areas, including the financial field, and proved to be efficient in solving various problems, including the prediction of the default probability of a customer to meet their obligations to the bank, using classification algorithms. Their output is further used when deciding whether to approve a loan or no, based on the previous behavior of the customers, hence reduces the loss of the bank. Even though Machine Learning algorithms proved to be efficient in solutioning this type of problems, none was identified for remarkable results. This paper studies 10 different methods applied on the same dataset (Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Kernel Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, Bagging Classifier, Linear Discriminant Analysis, Neural Network - Multi Layer Perceptron) and performs a comparative analysis aiming to identify the one which outperforms the others. Their performance is evaluated based on some well-known statistical measures such as Accuracy, Misclassification Rate, Precision and Specificity. In addition, this paper also presents and evaluates the impact of feature selection on the overall performance of an algorithm.

Efficient Data Mining Techniques for Heart Disease Prediction and Comparative Analysis of Classification Algorithms

Data mining techniques are used to extract interesting patterns and discover meaningful knowledge from huge amount of data. There has been increasing in usage of data mining techniques on medical data for determining useful trends and patterns that are used in analysis and decision making. About eighty percent of human deaths occurred in low and middle-income countries due to heart diseases. The healthcare industry generates large amount of heart disease data which are not organized. These data make the prediction process more complicated and voluminous. Data mining provides the techniques for fast and accurate transformation of data into useful information for heart diseases prediction. The main objectives of this research is to predict heart diseases more accurately using Naïve Bayes, J48 Decision Tree, Neural Network, Random Forest classification algorithms and compare the performance of classifiers. The research uses raw dataset for performance analysis and the analysis is based on Weka Tool. This research also shows best technique from them which is Random Forest on the basis of accuracy and execution time.

A Comparative Analysis of Classification Algorithms in Liver Disease Detection

A Comparative Analysis of Classification Algorithms in Liver Disease Detection

Comparative Analysis of Classification Algorithms in the Analysis of Medical Images From Speckle Tracking Echocardiography Video Data

Background. Machine learning allows applying various intelligent algorithms to produce diagnostic and/or prognostic models. Such models can be used to determine the functional state of the heart, which is diagnosed by speckle-tracking echocardiography. To determine the patient's heart condition in detail, a classification approach is used in machine learning. Each of the classification algorithms has a different performance when applied to certain situations. Therefore, the actual task is to determine the most efficient algorithm for solving a specific task of classifying the patient's heart condition when applying the same speckle-tracking echocardiography data set. Objective. We are aimed to evaluate the effectiveness of the application of prognostic models of logistic regression, the group method of data handling (GMDH), random forest, and adaptive boosting (AdaBoost) in the construction of algorithms to support medical decision-making on the diagnosis of coronary heart disease. Methods. Video data from speckle-tracking echocardiography of 40 patients with coronary heart disease and 16 patients without cardiac pathology were used for the study. Echocardiography was recorded in B-mode in three positions: long axis, 4-chamber, and 2-chamber. Echocardiography frames that reflect the systole and diastole of the heart (308 samples in total) were taken as objects for classification. To obtain informative features of the selected objects, the genetic GMDH approach was applied to identify the best structure of harmonic textural features. We compared the efficiency of the following classification algorithms: logistic regression method, GMDH classifier, random forest method, and AdaBoost method. Results. Four classification models were constructed for each of the three B-mode echocardiography positions. For this purpose, the data samples were divided into 3: training sample (60%), validation sample (20%), and test sample (20%). Objective evaluation of the models on the test sample showed that the best classification method was random forest (90.3% accuracy on the 4-chamber echocardiography position, 74.2% on the 2-chamber, and 77.4% on the long axis). This was also confirmed by ROC analysis, wherein in all cases, the random forest was the most effective in classifying cardiac conditions. Conclusions. The best classification algorithm for cardiac diagnostics by speckle-tracking echocardiography was determined. It turned out to be a random forest, which can be explained by the ensemble approach of begging, which is inherent in this classification method. It will be the mainstay of further research, which is planned to be performed to develop a full-fledged decision support system for cardiac diagnostics.

Comparative analysis of classification algorithms for critical land prediction in agricultural cultivation areas

Currently, the identification of critical land, that has been physically, chemically, and biologically damaged, uses a geographic information system. However, it requires a high cost to get the high resolution of satellite images. In this study, a comparison framework is proposed to determine the performance of the classification algorithms, namely C.45, ID3, Random Forest, k-Nearest Neighbor, and Naïve Bayes. This research aims to find out the best algorithm for the classification of critical land in agricultural cultivation areas. The results show that the highest accuracy Random Forest algorithm was 93.10 % in predicting critical land, and the naïve Bayes has the lowest performance, with 89.32 % of accuracy in predicting critical land.

Comparative Analysis of Classification Algorithms Using CNN Transferable Features: A Case Study Using Burn Datasets from Black Africans

Burn is a devastating injury affecting over eleven million people worldwide and more than 265,000 affected individuals lost their lives every year. Low- and middle-income countries (LMICs) have surging cases of more than 90% of the total global incidences due to poor socioeconomic conditions, lack of preventive measures, reliance on subjective and inaccurate assessment techniques and lack of access to nearby hospitals. These factors necessitate the need for a better objective and cost-effective assessment technique that can be easily deployed in remote areas and hospitals where expertise and reliable burn evaluation is lacking. Therefore, this study proposes the use of Convolutional Neural Network (CNN) features along with different classification algorithms to discriminate between burnt and healthy skin using dataset from Black-African patients. A pretrained CNN model (VGG16) is used to extract abstract discriminatory image features and this approach was due to limited burn images which made it infeasible to train a CNN model from scratch. Subsequently, decision tree, support vector machines (SVM), naïve Bayes, logistic regression, and k-nearest neighbour (KNN) are used to classify whether a given image is burnt or healthy based on the VGG16 features. The performances of these classification algorithms were extensively analysed using the VGG16 features from different layers.

A Comparative Analysis of Classification Algorithms on Students’ Performance

Recently educational system, many features control a student’s performance. Students should be well stimulated to study their education. Motivation leads to interest, interest leads to success in their lives. Appropriate assessment of abilities encourages the students to do better in their education. Data mining is to find out patterns by analyzing a large dataset and apply those patterns to predict the possibility of the future events. Data mining is a very critical field in educational area and it provides high potential for the schools and universities. In data mining, there are various classification techniques with various levels of accuracy. This paper focuses to make comparative evaluation of four classifiers such as J48, Naive Bayesian, Bayesian Network and Decision Stump by using WEKA tool. This study is to investigate and identify the best classification technique to analyze and predict the students’ performance of University of Jordan.

The comparative analysis on the accuracy of k-NN, Naive Bayes, and Decision Tree Algorithms in predicting crimes and criminal actions in Sleman Regency

Crime is an action which is considered as a violation of law and can harm others. Nowadays, crimes has increased with erratic patterns. Therefore, crime prevention is necessary since it will occur based on the historical data. Data mining is a technique that can be used to predict crimes that will occur. According to the previous researches, data mining techniques have several methods that can be used to predict crimes by utilizing the data of crimes that have occurred. Hence, it is necessary to conduct a comparative analysis of classification algorithms in order to obtain accurate prediction results based on the crime data in Sleman regency. The classification algorithms analyzed in this study were k-NN, Naive Bayes, and Decision Tree. Based on the three algorithms, the accuracy of k-NN with k = 5 was 57.88 percent, with k = 10 was 59.49 percent, with k = 15 is 59.38 percent, with k = 20 was 60.18 percent, and with k = 25 was 61.57 percent. Meanwhile, for the Naive Bayes algorithm, the accuracy reached 65.59 percent, and the Decision Tree algorithm reached 60.23 percent. In conclusion, the algorithm with the highest accuracy was owned by Naive Bayes.

Comparative analysis of classification algorithms for chronic kidney disease diagnosis

Chronic Kidney Disease (CKD) is one of the leading cause of death contributed by other illnesses such as diabetes, hypertension, lupus, anemia or weak bones that lead to bone fractures. Early prediction of CKD is important in order to contain the disesase. However, instead of predicting the severity of CKD, the objective of this paper is to predict the diagnosis of CKD based on the symptoms or attributes observed in a particular case, whether the stage is acute or chronic. To achieve this, a classification model is proposed to label stage of severity for kidney diseases patients. The experiments then investigated the performance of the proposed classification model based on eight supervised classification algorithms, which are ZeroR, Rule Induction, Support Vector Machine, Naïve Bayes, Decision Tree, Decision Stump, k-Nearest Neighbour, and Classification via Regression. The performance of the all classifiers is evaluated based on accuracy, precision, and recall. The results showed that the regression classifier perform best in the kidney diagnostic procedure.

Automated system of recognition of road signs for ADAS systems

Advanced Driver Assistance Systems (ADAS) require an environmental recognition function to inform the driver and for making management decisions. To solve the problems of pattern recognition, it is necessary to use one of the image classification methods. To date, there are quite a few similar algorithms that differ in the quality and speed of recognition. This paper presents a comparative analysis of classification algorithms in the problems of road signs recognition based on the GTSRB dataset. The results of the work showed that the most promising methods of classification in the problems of image recognition are the methods of reference vectors and k – nearest neighbors.

Algorithm Tuning from Comparative Analysis of Classification Algorithms

Algorithm Tuning from Comparative Analysis of Classification Algorithms

A Comparative Analysis of Classification Algorithms on Diverse Datasets

A Comparative Analysis of Classification Algorithms on Diverse Datasets

A Comparative Analysis of Classification Algorithms on Diverse Datasets

Data mining involves the computational process to find patterns from large data sets. Classification, one of the main domains of data mining, involves known structure generalizing to apply to a new dataset and predict its class. There are various classification algorithms being used to classify various data sets. They are based on different methods such as probability, decision tree, neural network, nearest neighbor, boolean and fuzzy logic, kernel-based etc. In this paper, we apply three diverse classification algorithms on ten datasets. The datasets have been selected based on their size and/or number and nature of attributes. Results have been discussed using some performance evaluation measures like precision, accuracy, F-measure, Kappa statistics, mean absolute error, relative absolute error, ROC Area etc. Comparative analysis has been carried out using the performance evaluation measures of accuracy, precision, and F-measure. We specify features and limitations of the classification algorithms for the diverse nature datasets.

Comparative Analysis of Classification Algorithms for Email Spam Detection

Comparative Analysis of Classification Algorithms for Email Spam Detection

Comparative Analysis of Classification Algorithms on Endometrial Cancer Data

Objective: To expose the Performance of classification algorithms on endometrial cancer data. The best algorithms are listed based on the result of various test options and ranked based on their accuracies. Methods and Analysis: Classification is one of the data mining techniques used to find a model that describes the data classes or concepts. The class-label of strange instance is predicted with the help of classification. It compares the classification algorithms by measuring accuracies, speed and strength of algorithms using WEKA tool. Accuracies of classification algorithms are calculated by means of four different options. The error rate and time taken to build the model also measured. Findings: The accuracies of sixteen algorithms are measured by training set, test set, tenfold cross validation and percentage split testing options. The average accuracies are calculated, then compared and ranked with highest accuracy first. The best five algorithms are taken for final performance on endometrial cancer dataset. The accuracy of Random Forest algorithm is high, but it took 0.16 sec to build the model, whereas the IBK, Random Tree and KStar algorithms’ performs well with 0sec to build the model. Bagging algorithm takes more time to build the model. In terms of time and accuracy IBK produces better results as compared to other algorithms. Random Forest algorithm is most excellent in provisos of correctly classified occurrence. Novelty/Improvement: With the 315 instances of endometrial cancer data, the time taken to build the model is zero for IBK, KStar and Random Tree algorithms. If the number of instance increases then time also will increase.

A comparative analysis of approaches for successional vegetation classification in the Brazilian Amazon

Research on separation of successional stages has been an active topic for the past two decades because successional vegetation plays an important role in the carbon budget and restoration of soil fertility in the Brazilian Amazon. This article examines classification of successional stages by conducting a comparative analysis of classification algorithms (maximum likelihood classifier – MLC, artificial neural network – ANN, K-nearest neighbour – KNN, support vector machine – SVM, classification tree analysis – CTA, and object-based classification – OBC) on varying remote-sensing data-sets (Landsat and ALOS PALSAR). Through this research we obtained the following four major conclusions: (1) Landsat data provide higher classification accuracy than ALOS PALSAR data, and individual PALSAR data cannot effectively separate successional stages; (2) Fusion of Landsat and PALSAR data provides better classification than individual sensor data; (3) Depending on the data-set, the best classification algorithm varies, MLC and CTA are recommended for Landsat or fusion images; and KNN is recommended for the combination of Landsat and PALSAR data as extra bands; (4) the MLC based on fusion images is recommended for vegetation classification in the moist tropical region when sufficiently representative training samples are available.