AIMC Topic: Logistic Models

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Predictive analysis on the factors associated with birth Outcomes: A machine learning perspective.

International journal of medical informatics
BACKGROUND: Recent studies reveal that around 1.9 million stillbirths occur annually worldwide, with Sub-Saharan Africa having among the highest cases. Some Sub-Saharan African countries, including Ghana, failed to meet Millennium Development Goal 5 ...

Cardiac patients' surgery outcome and associated factors in Ethiopia: application of machine learning.

BMC pediatrics
INTRODUCTION: Cardiovascular diseases are a class of heart and blood vessel-related illnesses. In Sub-Saharan Africa, including Ethiopia, preventable heart disease continues to be a significant factor, contrasting with its presence in developed natio...

Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiovascular disease (CD) is a major global health concern, affecting millions with symptoms like fatigue and chest discomfort. Timely identification is crucial due to its significant contribution to global mortality. In h...

A Data-Driven Approach to Predicting Recreational Activity Participation Using Machine Learning.

Research quarterly for exercise and sport
With the popularity of recreational activities, the study aimed to develop prediction models for recreational activity participation and explore the key factors affecting participation in recreational activities. A total of 12,712 participants, exc...

Machine-learning-based models assist the prediction of pulmonary embolism in autoimmune diseases: A retrospective, multicenter study.

Chinese medical journal
BACKGROUND: Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancin...

Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning.

Nature communications
Functionally relevant coronary artery disease (fCAD) can result in premature death or nonfatal acute myocardial infarction. Its early detection is a fundamentally important task in medicine. Classical detection approaches suffer from limited diagnost...

Machine learning models' assessment: trust and performance.

Medical & biological engineering & computing
The common black box nature of machine learning models is an obstacle to their application in health care context. Their widespread application is limited by a significant "lack of trust." So, the main goal of this work is the development of an evalu...

A Kernelized Classification Approach for Cancer Recognition Using Markovian Analysis of DNA Structure Patterns as Feature Mining.

Cell biochemistry and biophysics
Nucleotide-based molecules called DNA and RNA are essential for several biological processes that affect both normal and cancerous cells. They contain the critical genetic material needed for normal cell growth and functioning. The DNA structure patt...

Development and external validation of a logistic and a penalized logistic model using machine-learning techniques to predict suicide attempts: A multicenter prospective cohort study in Korea.

Journal of psychiatric research
Despite previous efforts to build statistical models for predicting the risk of suicidal behavior using machine-learning analysis, a high-accuracy model can lead to overfitting. Furthermore, internal validation cannot completely address this problem....