openheart.bmj

Risk prediction modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: leveraging machine learning

Background Preprocedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-making and benchmarking hospital performance. This study ...
IEEE

Accuracy Comparison of Logistic Regression, Random Forest, and Neural Networks Applied to Real MaaS Data

Abstract: The paper deals with a comparative analysis of three widely used data analysis methods: logistic regression, random forest, and neural networks. These methods have been evaluated in terms of ...
Open Heart

Artificial intelligence-based clustering to identify functional risk phenotypes in heart failure

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...
Scientific Research Publishing

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ()

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
IEEE

A Logistic Regression Based Credit Risk Assessment Using WoE Bining and Enhanced Feature Engineering Approach ANOVA and Chi-Square

Abstract: This research paper presents a comprehensive approach to build a data-driven credit risk model using machine learning techniques. Despite advancements in risk assessment methods, loan ...
GitHub

Hand-Drawn Shape Classifier

This project aims to detect and classify 16x16 pixel drawings into 10 categories (Sun, Moon, Tree, etc.) using linear and probabilistic models. The main focus was not just to use high-level libraries, ...