Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...

The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over ...

Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as the Framingham ...

Abstract: A commonly recognized chronic metabolic condition known as Diabetes mellitus significantly affects the global, social and economic standing of people. Obesity, age, high blood pressure, ...

ABSTRACT: This study presents a comparative analysis of machine learning models for threat detection in Internet of Things (IoT) devices using the CICIoT2023 dataset. We evaluate Logistic Regression, ...

Abstract: With the increasing importance of digital security in the current world of finance, it is a must to find ways to implement artificial intelligence techniques to detect financial fraud ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

Additionally, hybrid models (e.g., LR+NB, RF+LR) are tested to identify the most effective approach for early Parkinson’s Disease (PD) detection.

ABSTRACT: The Efficient Market Hypothesis postulates that stock prices are unpredictable and complex, so they are challenging to forecast. However, this study demonstrates that it is possible to ...