Heart Plaque detection with improved accuracy using Logistic Regression and comparing with Least Squares Support Vector Machine

Purpose: Customer churn is a crucial issue for companies, especially those in the telecommunications sector, as it has a direct impact on revenue and new customer acquisition costs. The purpose of this research is to create a customer churn prediction model through performance comparison between the Logistic Regression algorithm and Ridge Classifier, considering the effect of data balancing. Methods: This study developed a churn classification model by comparing the Logistic Regression and Ridge Classifier algorithms in three scenarios: without data balancing, balancing using SMOTE, and balancing using GAN. The dataset used was Telco Customer Churn from Kaggle. Model evaluation was performed using a confusion matrix with accuracy, precision, recall, and F1-score metrics, with a primary focus on the accuracy metric. Result: The results show that data balancing using SMOTE and GAN does not improve model accuracy. The highest accuracy was achieved by the Ridge Classifier without data balancing, at 82.47%, followed by Logistic Regression at 82.25%. However, the recall and F1-score metrics improved when using SMOTE. The highest recall was achieved by Ridge Classifier at 75.34% and Logistic Regression at 75.07% in the SMOTE 50:50 scenario. The highest F1-score was also achieved by Ridge Classifier at 64.76% and Logistic Regression at 64.68% followed by the SMOTE 50:30 scenario. Meanwhile, the precision metric tends to decrease after data balancing. Novelty: The uniqueness of this study lies in the comparison of the performance of the Ridge Classifier and Logistic Regression in data balancing scenarios using SMOTE and GAN, which has not been widely discussed in previous studies. The main findings show that the highest accuracy is achieved when the Ridge Classifier model uses original data or without applying SMOTE or GAN data balancing. However, data balancing using SMOTE has been proven to significantly improve the recall and F1-score metrics.

The government has launched the latest innovation in data collection in the realm of population data which relies on digital technology through mobile applications using photos or QR codes which aims to reduce the use of physical prints of identity cards and the availability of blank KTPs with the aim of simplifying the administrative process and no longer requiring population documents. printing or saving in physical format such as an KTP file. In implementing the population identity application, some people feel anxious due to limited internet access, lack of knowledge about the application, as well as concerns about the security and privacy of identity data in digital format. This research aims to conduct sentiment analysis on reviews of digital population identity applications by comparing logistic regression and k-nearest neighbor algorithms. The dataset was taken using the Google Play Scraper library in Python which got 1700 raw data taken from 12-February to 26 March 2024 and then pre-processed and got 1108 clean data. The results of this research show that the comparison between the logistic regression algorithm and k-nearest neighbor algorithm shows that the k-nearest neighbor algorithm is better than the logistic regression algorithm with an accuracy result of 80.43%, a difference of 3.60% compared to k-nearest neighbor. So it can be concluded that the digital population identity application is still considered poor in its use because it has a negative sentiment of 73.9% and it can be seen in this research that the comparison results of the k-nearest neighbor algorithm prove that its performance is better than logistic regression

This study aimed to validate the accuracy and prediction performance of machine learning (ML), deep learning (DL), and logistic regression methods in the treatment of medial meniscus posterior root tears (MMPRT). From July 2003 to May 2018, 640 patients diagnosed with MMPRT were included. First, the affecting factors for the surgery were evaluated using statistical analysis. Second, AI technology was introduced using X-ray and MRI. Finally, the accuracy and prediction performance were compared between ML&DL and logistic regression methods. Affecting factors of the logistic regression method corresponded well with the feature importance of the six top-ranked factors in the ML&DL method. There was no significant difference when comparing the accuracy, F1-score, and error rate between ML&DL and logistic regression methods (accuracy = 0.89 and 0.91, F1 score = 0.89 and 0.90, error rate = 0.11 and 0.09; p = 0.114, 0.422, and 0.119, respectively). The area under the curve (AUC) values showed excellent test quality for both ML&DL and logistic regression methods (AUC = 0.97 and 0.94, respectively) in the evaluation of prediction performance (p = 0.289). The affecting factors of the logistic regression method and the influence of the ML&DL method were not significantly different. The accuracy and performance of the ML&DL method in predicting the fate of MMPRT were comparable to those of the logistic regression method. Therefore, this ML&DL algorithm could potentially predict the outcome of the MMRPT in various fields and situations. Furthermore, our method could be efficiently implemented in current clinical practice.

Objective A predictive model was established based on logistic regression and XGBoost algorithm to investigate the factors related to postoperative hypocalcemia in patients with secondary hyperparathyroidism (SHPT). Methods A total of 60 SHPT patients who underwent parathyroidectomy (PTX) in our hospital were retrospectively enrolled. All patients were randomly divided into a training set (n = 42) and a test set (n = 18). The clinical data of the patients were analyzed, including gender, age, dialysis time, body mass, and several preoperative biochemical indicators. The multivariate logistic regression and XGBoost algorithm models were used to analyze the independent risk factors for severe postoperative hypocalcemia (SH). The forecasting efficiency of the two prediction models is analyzed. Results Multivariate logistic regression analysis showed that body mass (OR = 1.203, P = 0.032), age (OR = 1.214, P = 0.035), preoperative PTH (OR = 1.026, P = 0.043), preoperative Ca (OR = 1.062, P = 0.025), and preoperative ALP (OR = 1.031, P = 0.027) were positively correlated with postoperative SH. The top three important features of XGBoost algorithm prediction model were preoperative Ca, preoperative PTH, and preoperative ALP. The area under the curve of the logistic regression and XGBoost algorithm model in the test set was 0.734 (95% CI: 0.595~0.872) and 0.827 (95% CI: 0.722~0.932), respectively. Conclusion The predictive models based on the logistic regression and XGBoost algorithm model can predict the occurrence of postoperative SH.

Machine learning (ML) is combined with noninvasive parameters from coronary computed tomography angiography (CTA) to construct predictive models to identify culprit lesions that may lead to acute coronary syndrome (ACS). We retrospectively analyzed 132 patients with ACS at the Fourth Affiliated Hospital of Harbin Medical University who had coronary CTA between 3 months and 3 years before the ACS event, with a total of 240 lesions. Lesions from 2020 (n=154) were included in the training set, and lesions from 2021 (n=86) were included in the test set for internal validation. We evaluated the role of plaque characteristics, hemodynamic parameters and pericoronary adipose tissue (PCAT) attenuation from CTA in identifying culprit ACS lesions. In the training set, logistic regression was used to screen CTA-derived parameters with P values <0.05 for the model construction. Logistic regression, random forest, Bayesian and K-nearest neighbor algorithms were used to build classification models, and their performance was assessed using the test set. The following models were established to evaluate the effectiveness of different combinations of models to identify culprit lesions: Model 1 was established for plaque characteristics; Model 2 was established for hemodynamic parameters; Model 3 was established for PCAT attenuation; Model 4 was established for plaque characteristics and hemodynamic parameters; and Model 5 was established for plaque characteristics, hemodynamic parameters and PCAT attenuation. A total of ten high-risk factors were screened for the ML model construction, and the ML model based on the logistic regression algorithm had the best performance among the four algorithms (accuracy =0.721; sensitivity =0.892; specificity =0.592; positive prediction =0.623; and negative prediction =0.879). In this model, the minimum lumen area, positive remodeling and lesion-specific fat attenuation index (FAI) were the risk factors significantly associated with the culprit lesion. Analysis of the effect of different combinations of models to identify culprit lesions showed that Model 5 had the best predictive effect (AUC =0.819 and 95% CI: 0.722-0.916). ACS can be predicted using ML based on CTA parameters. Compared to other models, the model combining plaque characteristics, hemodynamic parameters and PCAT attenuation performed best in predicting the culprit lesion.

Flood disasters resulting from excessive water in stream beds inflict extensive damage. Floods are caused by the expansion of cities, the erosion of riverbeds, inadequate infrastructure, and increasing precipitation due to climate change. Floods cause great damage to agricultural areas and settlements. Regions that may be affected by floods should be identified, and precautions should be taken in these areas to prevent these damages. Flood susceptibility maps are produced for this reason. The purpose of this study was to construct a flood susceptibility map so that susceptible locations in Mersin may be identified. Firstly, 429 flood events were identified for the flood inventory map. Twelve conditioning factors, namely elevation, slope, distance to river, distance to drainage, drainage density, soil permeability, precipitation, land cover/land use, stream power index (SPI), topographic wetness index (TWI), aspect, and curvature were used to create flood susceptibility maps, applying logistic regression and best-worst methods. The flood inventory data were used to prepare susceptibility maps and test their consistency. The receiver operating characteristic (ROC) curve was used for consistency analysis. In logistic regression, 86% of floods were located within 20% of the study area that was categorized as high and very high susceptibility. According to the value of the area under the ROC curve (AUC), logistic regression had a 0.901 value. Land use, soil permeability, and elevation were the most important factors in the logistic regression method. In the best-worst method, 85% of floods were located within the 14% of the study area categorized as high and very high susceptibility. According to the AUC value, the best-worst method had a 0.898 value. Elevation, distance to river, and precipitation factors had the highest coefficient value in the best-worst method. Based on the AUC values, the flood susceptibility maps had a high prediction capacity.

The main problem in producing high accuracy software defect prediction is if the data set has an imbalance class and dichotomous characteristics. The imbalanced class problem can be solved using a data level approach, such as resampling methods. While the problem of software defects predicting if the data set has dichotomous characteristics can be approached using the classification method. This study aimed to analyze the performance of the proposed software defect prediction method to identify the best combination of resampling methods with the appropriate classification method to provide the highest accuracy. The combination of the proposed methods first is the resampling process using oversampling, under-sampling, or hybrid methods. The second process uses the classification method, namely the Support Vector Machine (SVM) algorithm and the Logistic Regression (LR) algorithm. The proposed, tested model uses five NASA MDP data sets with the same number attributes of 37. Based on the t-test, the &lt; = 0.0344 &lt; 0.05 and the &gt; = 3.1524 &gt; 2.7765 which indicates that the combination of the proposed methods is suitable for classifying imbalanced class. The performance of the classification algorithm has also improved with the use of the resampling process. The average increase in AUC values using the resampling in the SVM algorithm is 17.19%, and the LR algorithm is at 7.26% compared to without the resampling process. Combining the three resampling methods with the SVM algorithm and the LR algorithm shows that the best combining method is the oversampling method with the SVM algorithm to software defects prediction in imbalanced class with an average accuracy value of 84.02% and AUC 91.65%.

Most optimization methods for logistic regression or maximum entropy solve the primal problem. They range from iterative scaling, coordinate descent, quasi-Newton, and truncated Newton. Less efforts have been made to solve the dual problem. In contrast, for linear support vector machines (SVM), methods have been shown to be very effective for solving the dual problem. In this paper, we apply coordinate descent methods to solve the dual form of logistic regression and maximum entropy. Interestingly, many details are different from the situation in linear SVM. We carefully study the theoretical convergence as well as numerical issues. The proposed method is shown to be faster than most state of the art methods for training logistic regression and maximum entropy.

This paper addresses the problem of cyberbullying on various online discussion forums in the form of social commentary. Here, supervised machine learning algorithms are employed to detect whether a particular comment is an insult, threat or a hate message. First of all, a machine learning model is developed with Logistic Regression, Random forest and naive bayes algorithms for classification and then, both Voting and AdaBoost classifiers are applied on the developed model to observe which works best in this case. In Japan, the members of PTA (Parent Teacher Association) perform net-petrol with a manual website monitoring in order to catch and stop cyberbullying activities; however, doing all this manually is very time consuming and hectic process. The main contribution of this paper includes a mechanism to detect cyberbullying and by using supervised machine learning with logistic regression algorithm, model has achieved an accuracy of 82.7%. With voting classifier, an accuracy of 84.4% was observed. The evaluation results show that voting classifier outperforms all other algorithms in detecting cyberbullying.

Anemia Before Coronary Artery Bypass Surgery as Additional Risk Factor Increases the Perioperative Risk

Increasing the accuracy and reproducibility of positron emission tomography radiomics for predicting pelvic lymph node metastasis in patients with cervical cancer using 3D local binary pattern-based texture features

The aim of this research is to identify the credit card fraud detection model using an improved K-nearest neighbor classifier method to find the credit card fraudulent transactions with accuracy percentage compared with Logistic regression algorithm. Materials and Methods: Logistic regression algorithm with sample size is 26 and K-nearest neighbor classifier method with sample size is 26 was iterated for predicting accuracy percentage of credit card fraud detection. The study contains two groups where the K-nearest neighbor classifier method algorithm is analysed in the first group, the logistic regression algorithm is analysed in the second group. The G power test value is 0.8 and the accuracies of each algorithm are compared. Results: Logistic regression algorithm is significantly better accuracy 92% compared to K-nearest neighbor 90% with the power value of 80% The two algorithms KNN and logistic regression are statistically not satisfied with the independent sample T-Test <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${(\alpha=.881)}$</tex> value <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${(\mathrm{p}=0.05)}$</tex> with a confidence level of 95%. Conclusion: The outcome of the proposed algorithm was compared with the K-nearest neighbor classifier method algorithm and the proposed algorithm has significantly better accuracy of 92% compared with Logistic regression algorithm.

General Background: Heart failure remains a global health concern due to its high prevalence and mortality rates. Specific Background: In Indonesia, heart disease ranks as a leading cause of death, emphasizing the need for reliable predictive tools. Knowledge Gap: Despite the availability of machine learning algorithms, limited studies provide a comparative evaluation using updated clinical datasets in the Indonesian context. Aims: This study aims to compare the performance of Logistic Regression and Support Vector Machine (SVM) in predicting heart failure. Results: Using a dataset of 918 samples and rigorous preprocessing, SVM achieved 90% accuracy with an AUC of 0.93, outperforming Logistic Regression, which scored 88% accuracy and an AUC of 0.9304. SVM demonstrated superior sensitivity and robustness in handling non-linear data, while Logistic Regression offered better calibration for risk interpretation. Novelty: The study’s novelty lies in its integrated open-source pipeline, use of biomedical signal features, and statistical validation via McNemar’s test. Implications: These findings support the implementation of SVM in automated clinical decision systems for early heart failure detection, while highlighting Logistic Regression's value in interpretability-focused clinical settings.Highlight : SVM has higher accuracy and F1-score, indicating superior classification performance. Logistic Regression remains superior in probability calibration, important for clinical risk interpretation. Both models effectively support the development of machine learning-based health prediction systems. Keywords : Logistic Regression, Support Vector Machine (SVM), Heart Failure, Machine Learning Classification, Prediction Calibration

In this article, the Internet of Medical Things (IoMT) framework based on Apache Spark big data processing technology is proposed for real‐time analysis of health data obtained from wireless body area networks (WBANs), which is one of the most important components of IoMT. The proposed framework consists of four layers: data source, data collection, data analytics and visualization. In addition, the proposed IoMT framework is presented with two different disease prediction scenarios, diabetes and heart disease. Diabetes and heart disease prediction processes are carried out using the random forest (RF), logistic regression (LR) and support vector machine (SVM) algorithms belonging to the Apache Spark machine learning library (MLlib). The analysis of health data generated in WBANs takes place in real‐time in the Apache Spark‐based data analytics layer. In this study, the performances of MLlib algorithms in the real‐time model developed for heart and diabetes disease are examined. The SVM algorithm with an accuracy rate of 93.33% for heart disease and the LR algorithm with an accuracy rate of 78.89% for diabetes are found to provide the best performances.

To assess the predictive ability of a multi-parametric MRI-based radiomics signature (RS) for the preoperative evaluation of Ki-67 proliferation status in sinonasal malignancies. A total of 128 patients with sinonasal malignancies that underwent multi-parametric MRIs at two medical centres were retrospectively analysed. Data from one medical centre (n = 77) were used to develop the predictive models and data from the other medical centre (n = 51) constitute the test dataset. Clinical data and conventional MRI findings were reviewed to identify significant predictors. Radiomics features were determined using maximum relevance minimum redundancy and least absolute shrinkage and selection operator algorithms. Subsequently, RSs were established using a logistic regression (LR) algorithm. The predictive performance of RSs was assessed using calibration, decision curve analysis (DCA), accuracy, and AUC. No independent predictors of high Ki-67 proliferation were observed based on clinical data and conventional MRI findings. RS-T1, RS-T2, and RS-T1c (contrast enhancement T1WI) were established based on a single-parametric MRI. RS-Combined (combining T1WI, FS-T2WI, and T1c features) was developed based on multi-parametric MRI and achieved an AUC and accuracy of 0.852 (0.733-0.971) and 86.3%, respectively, on the test dataset. The calibration curve and DCA demonstrated an improved fitness and benefits in clinical practice. A multi-parametric MRI-based RS may be used as a non-invasive, dependable, and accurate tool for preoperative evaluation of the Ki-67 proliferation status to overcome the sampling bias in sinonasal malignancies. • Multi-parametric MRI-based radiomics signatures (RSs) are used to preoperatively evaluate the proliferation status of Ki-67 in sinonasal malignancies. • Radiomics features are determined using maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms. • RSs are established using a logistic regression (LR) algorithm.