Análisis comparativo de K-NN, Naïve-Bayes y regresión logística para la detección de fraude con tarjetas de crédito

Introduction: This paper highlights the outcome of the comparative study of “Various Machine learning algorithms namely K-NN, Naive Bayes, and Logistic Regression for Credit Card Fraud Detection” carried out based on a dataset taken from UCI.com in 2022-23 at Manav Rachna International Institute of Research and Studies. Problem: Credit card fraud is still rife today and the modes are increasingly varied. Quite often we hear of fraud cases that cause irreplaceable injury to banks and financial institutions which cannot be compensated in terms of costs. To avoid scams with various modes of credit cards, we must be able to identify and find out the modes often used by fraudsters. This scheme liberates such financial institutions and banks with complete and appropriate information using Machine Learning Techniques, not only about the modes that scammers or fraudsters often use but also ways to protect against such frauds. Objective: The present paper discusses the various machine learning models based on classification and regression, namely K-Nearest Neighbors, Naïve Bayes, and Logistic Regression, which are successfully able to achieve the classification accuracy of 80% using Logistic Regression with a Precision of 78%, Recall of 100%, and F1-Score of 88% for fraudulent credit card transactions. Methodology: The comparative analysis demonstrates that for Precision, Recall, and Accuracy parameters, the K-Nearest Neighbor is a better approach for detecting fraudulent transactions than the Logistic Regression and Naïve Bayes. Results: The accuracy is marginal high in Logistic Regression but the False Positive parameters are not able to identify the imbalanced data; therefore, they disguise the results and accuracy of Logistic Regression and K-Nearest Neighbor deems fit for such cases. Conclusion: This scheme depicts the automated fraud classification systems using machine learning techniques, namely K-Nearest Neighbor, Logistic Regression, and Naive Bayes, to produce a model that can distinguish valid and invalid credit card transactions. Originality: Through this research, the most relevant features are used to go through the visualization of accuracy with the confusion matrix, and accuracy calculations are obtained from the dataset used.Limitations: Deep learning techniques could have been used to fetch even better results.Introducción: este artículo muestra el resultado de un estudio comparativo de “varios algoritmos de machine learning, a saber, K-NN, Naïve-Bayes y regresión logística para la detección de fraudes con tarjetas de crédito”, realizado con base en un conjunto de datos tomado de UCI.com en 2022-23 en el Instituto Internacional de Investigaciones y Estudios Manav Rachna. Problema: el fraude con tarjetas de crédito está muy extendido hoy en día y las modalidades son cada vez más variadas. A menudo, se oye hablar de casos de fraude que causan daños irreparables a bancos e instituciones financieras, que no pueden ser compensados en términos de costos. Para evitar estafas con diversos modos de tarjetas de crédito, se debe poder identificar y descubrir los modos que suelen utilizar los estafadores. Este esquema proporciona a dichas instituciones financieras y bancos información completa y adecuada utilizando técnicas de machine learning, no solo sobre los modos que suelen utilizar los estafadores o defraudadores, sino también sobre las formas de protegerse contra dichos fraudes. Objetivo: el presente artículo analiza los diversos modelos de machine learning basados en clasificación y regresión, a saber, K-Nearest Neighbors (K-NN), Naïve Bayes y regresión logística, que pueden lograr con éxito una precisión de clasificación del 80% utilizando regresión logística con una precisión de 78%, Retiro del 100% y F1 Score del 88% para transacciones fraudulentas con tarjeta de crédito. Método: el análisis comparativo muestra que, para los parámetros de precisión, recuperación y exactitud, el K-NN es un mejor enfoque para detectar transacciones fraudulentas que la regresión logística y el Naïve Bayes.Resultados: la precisión es marginalmente alta en la regresión logística, pero los parámetros de falso positivo no pueden identificar los datos desequilibrados; por lo tanto, disfrazan los resultados y la precisión de la regresión logística y el K-NN se considera adecuado para tales casos. Conclusión: este esquema describe los sistemas automatizados de clasificación de fraude que utilizan técnicas de machine learning, a saber, K-NN, Regresión logística y Naïve Bayes, para producir un modelo que pueda distinguir transacciones con tarjetas de crédito válidas e inválidas. Originalidad: a través de esta investigación, se utilizan las características más relevantes para visualizar la precisión con la matriz de confusión y se obtienen cálculos de precisión a partir del conjunto de datos utilizado.Limitaciones: se podrían haber utilizado técnicas de Deep learning para obtener mejores resultados

Effectiveness of Non-nucleoside Reverse-Transcriptase Inhibitor-Based Antiretroviral Therapy in Women Previously Exposed to a Single Intrapartum Dose of Nevirapine: A Multi-country, Prospective Cohort Study

In a comparative cohort study, Jeffrey Stringer and colleagues investigate the risk of ART failure in women who received single-dose nevirapine for PMTCT, and assess the duration of increased risk

Bone Mineral Density in HIV-Negative Men Participating in a Tenofovir Pre-Exposure Prophylaxis Randomized Clinical Trial in San Francisco

Pre-exposure prophylaxis (PrEP) trials are evaluating regimens containing tenofovir-disoproxil fumarate (TDF) for HIV prevention. We determined the baseline prevalence of low bone mineral density (BMD) and the effect of TDF on BMD in men who have sex with men (MSM) in a PrEP trial in San Francisco.We evaluated 1) the prevalence of low BMD using Dual Energy X-ray Absorptiometry (DEXA) in a baseline cohort of 210 HIV-uninfected MSM who screened for a randomized clinical trial of daily TDF vs. placebo, and 2) the effects of TDF on BMD in a longitudinal cohort of 184 enrolled men. Half began study drug after a 9-month delay to evaluate changes in risk behavior associated with pill-use. At baseline, 20 participants (10%) had low BMD (Z score≤-2.0 at the L2-L4 spine, total hip, or femoral neck). Low BMD was associated with amphetamine (OR = 5.86, 95% CI 1.70-20.20) and inhalant (OR = 4.57, 95% CI 1.32-15.81) use; men taking multivitamins, calcium, or vitamin D were less likely to have low BMD at baseline (OR = 0.26, 95% CI 0.10-0.71). In the longitudinal analysis, there was a 1.1% net decrease in mean BMD in the TDF vs. the pre-treatment/placebo group at the femoral neck (95% CI 0.4-1.9%), 0.8% net decline at the total hip (95% CI 0.3-1.3%), and 0.7% at the L2-L4 spine (95% CI -0.1-1.5%). At 24 months, 13% vs. 6% of participants experienced >5% BMD loss at the femoral neck in the TDF vs. placebo groups (p = 0.13).Ten percent of HIV-negative MSM had low BMD at baseline. TDF use resulted in a small but statistically significant decline in BMD at the total hip and femoral neck. Larger studies with longer follow-up are needed to determine the trajectory of BMD changes and any association with clinical fractures.ClinicalTrials.gov: NCT00131677

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570