Ensemble framework for cardiovascular disease prediction
Heart disease is the major cause of non-communicable and silent death worldwide. Heart
diseases or cardiovascular diseases are classified into four types: coronary heart disease …
diseases or cardiovascular diseases are classified into four types: coronary heart disease …
Effective feature engineering technique for heart disease prediction with machine learning
Heart failure is a chronic disease affecting millions worldwide. An efficient machine learning-
based technique is needed to predict heart failure health status early and take necessary …
based technique is needed to predict heart failure health status early and take necessary …
Comparative analysis of machine learning algorithms for heart disease predictions
S Patidar, A Jain, A Gupta - 2022 6th International Conference …, 2022 - ieeexplore.ieee.org
The number one reason of deaths worldwide are cardiovascular diseases. An approximate
of 17.9 million lives die because of CVDs every year, which means it is responsible for 31 …
of 17.9 million lives die because of CVDs every year, which means it is responsible for 31 …
DASMcC: Data Augmented SMOTE Multi-class Classifier for prediction of Cardiovascular Diseases using time series features
N Sinha, MAG Kumar, AM Joshi… - IEEE Access, 2023 - ieeexplore.ieee.org
One of the leading causes of mortality worldwide is cardiovascular disease (CVD).
Electrocardiography (ECG) is a noninvasive and cost-effective tool to diagnose the heart's …
Electrocardiography (ECG) is a noninvasive and cost-effective tool to diagnose the heart's …
[HTML][HTML] A heart disease prognosis pipeline for the edge using federated learning
J Dheeba - e-Prime-Advances in Electrical Engineering …, 2024 - Elsevier
Cloud computing and edge computing have revolutionized deployments by giving virtually
unlimited computing and storage to ensure the scalability and availability of applications …
unlimited computing and storage to ensure the scalability and availability of applications …
Comparative Analysis of Machine Learning Models for Disease Prediction
A Chotrani - Journal of Science & Technology, 2022 - thesciencebrigade.com
The increasing availability of health-related data and advancements in machine learning
techniques have paved the way for the development of predictive models for disease …
techniques have paved the way for the development of predictive models for disease …
The study of artificial intelligent in risk-based inspection assessment and screening: A study case of inline inspection
T Aditiyawarman… - … -ASME Journal of …, 2023 - asmedigitalcollection.asme.org
The work reports the systematic approach to the study of artificial intelligence (AI) in
addressing the complexity of inline inspection (ILI) data management to forecast the risk in …
addressing the complexity of inline inspection (ILI) data management to forecast the risk in …
A pragmatic ensemble learning approach for rainfall prediction
S Ghosh, MK Gourisaria, B Sahoo, H Das - Discover Internet of Things, 2023 - Springer
Heavy rainfall and precipitation play a massive role in shaping the socio-agricultural
landscape of a country. Being one of the key indicators of climate change, natural disasters …
landscape of a country. Being one of the key indicators of climate change, natural disasters …
Deep learning based approach for milk quality prediction
This study aimed to find out what factors have the biggest impact on milk quality. We looked
at 9 different variables including pH, temperature, taste, odor, fat, turbidity, color, and grade …
at 9 different variables including pH, temperature, taste, odor, fat, turbidity, color, and grade …
Feature selection using differential evolution for microarray data classification
The dimensions of microarray datasets are very large, containing noise and redundancy.
The problem with microarray datasets is the presence of more features compared to the …
The problem with microarray datasets is the presence of more features compared to the …