Search Results for gaussian-naive-bayes

Abstract

Breast cancer is one of the most prevalent and life-threatening diseases worldwide, making early and accurate diagnosis essential for improving patient outcomes. This study aims to evaluate the performance of two machine learning models, Extreme Gradient Boosting (XGBoost) and Gaussian Naive Bayes, for breast cancer classification using the Breast Cancer Wisconsin Diagnostic dataset. A comparative analysis was conducted based on several performance metrics, including Accuracy, Precision, Recall, F1-score, Specificity, Receiver Operating Characteristic (ROC) curve, Area Under the Curve (AUC), Balanced Accuracy, and Confusion Matrix. In addition, feature importance analysis was performed to identify the most influential variables contributing to the classification process. The experimental results demonstrated that both models achieved high classification performance. However, the Gaussian Naive Bayes model showed slightly superior performance, achieving an Accuracy of 96.5% and an AUC of 0.997, compared with 96.0% and 0.993, respectively, for the XGBoost model. Furthermore, the analysis revealed that features related to tumor morphology, particularly concave points mean, perimeter worst, and radius worst, were among the most influential predictors of breast cancer classification. The findings indicate that both machine learning models provide reliable and effective tools for supporting early breast cancer diagnosis, with Gaussian Naive Bayes demonstrating a slight advantage for the dataset used in this study. These results highlight the potential of machine learning techniques to enhance diagnostic accuracy and support clinical decision-making.

Abstract

Given the swift digital changes occurring in the Banking industry, the purpose of this paper is to examine how well artificial intelligence systems can forecast and protect against future disasters.  By utilizing its skills in big data analytics, forecasting financial behavior, and more accurately and effectively managing risks, artificial intelligence (AI) is increasingly regarded as a crucial component in the development of banking systems and improving their operational efficiency.

 By enhancing client satisfaction, tailoring banking services to meet the demands of each individual, and cutting down on operational errors and administrative expenses, banks hope to gain a competitive edge by utilizing these technologies.  AI also helps to speed up credit decisions, make it possible to identify financial crime early, and create clever marketing plans based on forecasts of future market trends.

In order to ensure financial sustainability and achieve integration between digital transformation and the demands of banking innovation, studies show that the future of AI encompasses strategic, cultural, human, technological, and organizational dimensions in addition to technical ones.

 The paper also examined a number of anticipated long-term effects of AI applications, such as increased forecasting precision, lower operating expenses, better customer satisfaction, increased worker productivity, and assistance with investment choices.  The findings show that implementing AI applications in the banking sector is a strategic requirement to guarantee long-term growth and competitiveness in the digital era, not a technical luxury.

In order to enhance lending decisions and lower default risks, the paper also assesses how well a number of categorization algorithms work in assessing loan applicants' creditworthiness.  Using a dataset that represented the traits and financial activities of clients, seven machine learning techniques were used: Gradient Boosting, Random Forest, Extra Trees, Gaussian Naive Bayes, Logistic Regression, SVC-RBF, and KNN.

The paper used a database of 21 variables for loan applicants. Numerical variables included (age, income, credit score, debt-to-income ratio, and loan amount). Descriptive variables included (loan purpose, region, marital status, employer, educational level, and application channel). Binary variables included (whether or not the applicant had a history of default). These variables were used to predict the approval or rejection decision, with the dependent variable being represented by two values: 0 for rejection and 1 for approval.

The models were evaluated using the following six key performance indicators: Accuracy, Precision, Recall, F1 Score, Receiver Operating Characteristic Area Under the Curve (ROC AUC), and Brier Score.   The findings demonstrated that the Gradient Boosting algorithm performed best overall in both probability prediction quality and customer differentiation across different risk levels.  The Random Forest algorithm, which showed stability and balanced metrics, came next.  On the other hand, despite its moderate performance, Logistic Regression provided great interpretability, while the Gaussian Naive Bayes algorithm demonstrated high sensitivity in identifying high-risk customers.  In terms of overall accuracy and probability quality, some models—like SVC-RBF and KNN—performed worse.