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The primary purpose of this research is to use the Support Vector Machine (SVM) classifier and the Naive Bayes (NB) classifier to make arrhythmia predictions from the MIT-BIH database. With an alpha of 0.05, 95% confidence interval (CI), 80% power, and an enrollment ratio of 1, the proposed research employed SVM and NB machine learning algorithms to predict arrhythmia using the MIT-BIH dataset of 65 normal and 65 abnormal ECG signals downloaded from IEEE dataport in.xlsx format. We used the data mining programme WEKA 3.8.5 to determine how to categorise people with and without arrhythmia. IBM SPSS version 21 was used for the statistical analysis. When comparing SVM and NB classifiers, a statistically significant difference (p0.010) was found. Using WEKA's 10-fold cross-validation for training and testing, the SVM classifier outperformed the NB classifier with an 88.50% accuracy rate in classification (80.39 percent). [ABSTRACT FROM AUTHOR]

The primary goal of this research is to use J48 and K-Nearest Neighbor (KNN) classifiers to predict arrhythmia using the MIT-BIH database. With an alpha of 0.05, 95% confidence interval (CI), 80% power, and an enrollment ratio of 1, the proposed study used the J48 and KNN machine learning algorithms to predict arrhythmia using data from the MIT-BIH dataset consisting of healthy (n=65) and arrhythmia (n=65) ECG signals collected from IEEE dataport in.XLSX format. WEKA 3.8.5, a data mining tool, was used to distinguish between those with arrhythmia and those without. IBM SPSS version 21 was used for the statistical analysis. There was no discernible difference (p=0.025) between the J48 and KNN classifiers. Using WEKA's 10-fold cross validation to train, test, and verify the classifiers, we find that the J48 classifier is more accurate at classifying data (89.80 percent) than the KNN classifier (87.64 percent). [ABSTRACT FROM AUTHOR]

Aim: The aim of the study is to classify the benign and malignant tumor cells using support vector machines and logistic classifiers from Wisconsin dataset for the potential diagnostic application. Materials and Methods: Breast cancer report and data system (WDBC) with benign (n=21) and malignant (n=21) masses are collected from the kaggle machine learning repository for the proposed study. The dataset contains 21 attributes which are considered as inputs to our study. The classification of diseased and healthy subjects was performed using Waikato Environment for Knowledge Analysis (WEKA), a data mining tool. The statistical analysis was performed using IBM SPSS software. Results: The performance of the classifier was compared by an independent sample t-test using SPSS software. The statistical significant difference (p<0.05) was observed between the groups. The SVM classifier achieved the classification accuracy rate as 71.42%, which is higher than the Logistic classifier (97.61%). Conclusion: The Logistic classifier shows the higher accuracy value when compared to the SVM classifier for predicting the benign and malignant tumor cells using WDBC dataset. [ABSTRACT FROM AUTHOR]

Aim: The study's goal is to categorise benign and malignant tumour cells from the Wisconsin dataset using Random tree and K-nearest neighbour classifiers for possible diagnostic applications. Materials and Methods: Breast cancer report and data system (WDBC) with benign (n=21) and malignant (n=21) masses are collected from the kaggle machine learning repository for the proposed study. The dataset contains 21 attributes which are considered as inputs to our study. The classification of diseased and healthy subjects was performed using WEKA, a data mining tool. IBM SPSS software was used for the statistical analysis. The classifier's performance was compared using an independent sample t-test and SPSS software. There was a statistically significant difference (p0.05) between the groups. The KNN classifier achieved the classification accuracy rate as 95.23%, which is higher than the Random tree classifier (90.47%). Conclusion: The KNN classifier shows the higher accuracy value when compared to the Random tree classifier for predicting the benign and malignant tumor cells using WDBC dataset. [ABSTRACT FROM AUTHOR]

Aim: The aim of the study is to classify the diabetic and healthy subjects using Naive Bayes (NB) and K Nearest Neighbor (KNN) machine learning algorithms from a debrecen dataset. Materials and Methods: From the UCI machine learning repository, the debrecen dataset of healthy (n=34) and diabetic (n=34) subjects were collected. The dataset consists of 19 characteristics which are considered as the inputs to classifiers. The classification of healthy and diabetic subjects was performed using the Waikato Environment for Knowledge Analysis (Weka) 3.8.5 version data mining tool. The IBM Statistical Package for the Social Science (SPSS) version 21 software was used for the statistical analysis. Results: The performance of the two classifiers were compared and found that the accuracy obtained by Naive Bayes classifier (68.68%) appears to be higher than the K Nearest Neighbor classifier. The statistical significant difference was observed between the two groups (p<0.01). Conclusion: The Naive Bayes classifier has achieved a higher accuracy value than the K Nearest Neighbor classifier for the classification of healthy and diabetic subjects using a debreccan dataset. [ABSTRACT FROM AUTHOR]

Aim: The goal of this work is to use Logistic Model Tree (LMT) and Random Forest (RF) classifiers to differentiate between benign and malignant tumours. Materials and Procedures: Breast Image Report and Data System (BI-RADS) images with benign (n=27) and malignant (n=27) masses are acquired for the proposed study from the UCI machine learning repository. The data set comprises six qualities that are used as inputs to classifiers like LMT and RF. The Waikato Environment for Knowledge Analysis (WEKA), a data mining tool, is used to classify benign and malignant tumours. The Statistical Package for Social Sciences (SPSS) software is used for the statistical analysis. Results: The LMT classifier (p<0.01) is found to be more significant than the RF classifier with the classification accuracy rate as 77.77%. Conclusion: The LMT has achieved more accuracy than the RF classifier for classifying the benign and malignant masses. [ABSTRACT FROM AUTHOR]

Aim: The aim of the study is to classify the benign and malignant tumor cells using Bagging and Adaboost classifiers from Wisconsin dataset for the potential diagnostic application. Materials and Methods: Wisconsin Diagnostic Breast Cancer (WDBC) with benign (n=21) and malignant (n=21) masses are collected from the kaggle machine learning repository for the proposed study. The dataset contains 21 attributes which are considered as inputs to our study. The classification of diseased and healthy subjects was performed using WEKA, a data mining tool. The statistical analysis was performed using IBM SPSS software. Results: The performance of the classifier was compared by an independent sample t-test using SPSS software. The statistical insignificant difference (p>0.05) was observed between the groups. The Adaboost classifier achieved the classification accuracy rate as 92.85%, which is higher than the Bagging classifier (85.71%). Conclusion: The Adaboost classifier shows the higher accuracy value when compared to the Bagging classifier for predicting the benign and malignant tumor cells using WDBC dataset. [ABSTRACT FROM AUTHOR]

The aim of our study is to classify the lung nodule patients and healthy using k nearest neighbour (KNN) and logistic machine learning algorithms. The proposed study used the KNN and logistic machine learning algorithms to classify the lung nodule images using LIDC-IDRI dataset with normal (n=166) and abnormal (n=166) lung images which are collected from kaggle. We also used matlab software to extract the features from the lung nodule images and healthy lung images. The classification of diseased and healthy subjects were performed using WEKA 3.8.5, a data mining tool. The statistical analysis was performed using IBM SPSS software version 21. The statistical significant difference (p<0.01) was observed between two groups (KNN and Logistic). The classifier has been trained, tested and validated using 10-fold cross-validation in WEKA software, the KNN classifier has achieved a higher classification accuracy rate (98.49%) than Logistic classifier (79.81%). [ABSTRACT FROM AUTHOR]

The aim of this study is to classify the benign and malignant masses using J48 and K-Nearest Neighbors (KNN) classifiers. Breast Image Report and Data System (BI-RADS) with benign (n=27) and malignant (n=27) masses are collected from the UCI machine learning repository for the proposed study. The data set contains 6 attributes which are considered as inputs to the classifiers such as J48 and KNN. The classification of benign and malignant masses are done by using the Waikato Environment for Knowledge Analysis (WEKA), a data mining tool. The statistical analysis is performed using the Statistical Package for Social Sciences (SPSS) software. The J48 classifier (p=.000) is found to be more significant than KNN classifier with the classification accuracy rate as 81.48%. The J48 has achieved more accuracy than the K-Nearest Neighbors (KNN) classifier for classifying the benign and malignant masses. [ABSTRACT FROM AUTHOR]

The aim of the study is to classify the healthy and diabetic mellitus individuals by extracting textural features from left plantar thermograms and classifying using support vector machine (SVM) and Naive Bayes (NB) classifiers. Materials and Methods: Images are collected from IEEE dataport, machine learning repository for healthy (n=21) and abnormal (n=21) to our study with alpha value as 0.05, 95% as CI, power as 80% and enrollment ratio as 1. The classification of diseased and healthy subjects was performed using WEKA, a data mining tool. The statistical analysis was performed using IBM SPSS software. Results: The performance of the classifiers were compared and found that the NB classifier has achieved 90.47% as classification accuracy rate than the SVM classifier. The independent Tsample test reveals that there is a significant difference between the groups (p<0.01). Conclusion: The NB classifier shows the higher accuracy value when compared to the SVM classifier for predicting diabetes mellitus. [ABSTRACT FROM AUTHOR]

The main purpose of the study is to predict arrhythmia from the MIT-BIH database using Random Forest (RF) and Voted Perceptron (VP) classifiers. Materials and Methods: The proposed study uses the RF and VP Machine learning Algorithms to predict the arrhythmia using MIT-BIH dataset with healthy (n=65) and Arrhythmia (n=65) ECG signals collected from IEEE data port in.XLSX format for our study with alpha value as 0.05, 95% as CI, power as 80% and enrolment ratio as 1. The classification of arrhythmia and healthy subjects was performed using WEKA 3.8.5, a data mining tool. The statistical analysis was performed on IBM SPSS software version 21. RESULTS: The statistical significant difference (p<.000) was observed between RF and VP classifiers. Conclusion: The classifiers have been trained, tested, validated using 10 fold cross-validation in WEKA software, the innovative voted perceptron classifier has achieved a higher classification accuracy rate (88.86)% than RF classifier (87.06)%. [ABSTRACT FROM AUTHOR]