DEEP-CARDIO: Recommendation System for Cardiovascular Disease Prediction using IOT Network
A Yashudas, D Gupta, GC Prashant, A Dua… - IEEE Sensors …, 2024 - ieeexplore.ieee.org
The Internet of Things (IoTs)-based remote healthcare applications provide fast and
preventative medical services to the patients at risk. However, predicting heart disease is a …
preventative medical services to the patients at risk. However, predicting heart disease is a …
Heart disease detection using feature extraction and artificial neural networks: A sensor-based approach
AB Naeem, B Senapati, D Bhuva, A Zaidi… - IEEE …, 2024 - ieeexplore.ieee.org
This study presents a novel technique for identifying individuals using feature extraction
methods and signal processing approaches. It uses an artificial neural network (ANN) …
methods and signal processing approaches. It uses an artificial neural network (ANN) …
[HTML][HTML] A comparative assessment of most widely used machine learning classifiers for analysing and classifying autism spectrum disorder in toddlers and …
Individuals with autism spectrum disorder (ASD) have social interaction and communication
challenges due to a disruption in brain development that impacts how they perceive and …
challenges due to a disruption in brain development that impacts how they perceive and …
Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
Rice is one of the most widely consumed foods all over the world. Various diseases and
deficiency disorders impact the rice crop's growth, thereby hampering the rice yield …
deficiency disorders impact the rice crop's growth, thereby hampering the rice yield …
Novel framework of significant risk factor identification and cardiovascular disease prediction
S Bandyopadhyay, A Samanta, M Sarma… - Expert Systems with …, 2025 - Elsevier
Cardiovascular disease (CVD) remains a major public health concern, characterized by high
mortality rates and complex diagnostic challenges. Risk factor-based prediction models are …
mortality rates and complex diagnostic challenges. Risk factor-based prediction models are …
A Survey on Deep Learning Techniques for Predictive Analytics in Healthcare
Healthcare data is growing at more than 50% annually, making it one of the most rapidly
expanding data in the digital world. Clinical problem-solving is a difficult skill that doctors …
expanding data in the digital world. Clinical problem-solving is a difficult skill that doctors …
Grey wolf optimized stacked ensemble machine learning based model for enhanced efficiency and reliability of predicting early heart disease
G Narasimhan, A Victor - Automatika, 2024 - Taylor & Francis
Heart disease is one of the foremost reasons for death globally. Machine learning (ML) can
be used to predict heart diseases early, which can help improve patient outcomes. This …
be used to predict heart diseases early, which can help improve patient outcomes. This …
A Machine-Learning Approach to Detect Heart Disease
Moreover, heart disease has kept on being the leading cause of death worldwide, hence the
importance of coming up with different strategies for early detection and diagnosis. Machine …
importance of coming up with different strategies for early detection and diagnosis. Machine …
[HTML][HTML] Integrating predictive modeling and causal inference for advancing medical science
TR Oh - Childhood Kidney Diseases, 2024 - synapse.koreamed.org
Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease
prediction, diagnosis, and patient management. This review focuses on two key AI …
prediction, diagnosis, and patient management. This review focuses on two key AI …
Enhancing Heart Disease Prediction Using Artificial Neural Network with Preprocessing Techniques
R Mythili, AS Aneetha - … on Advancements in Smart Computing and …, 2023 - Springer
Heart disease and other cardiovascular disorders continue to be the most prevalent cause of
death. ML (Machine Learning) algorithms in particular have shown promise in forecasting for …
death. ML (Machine Learning) algorithms in particular have shown promise in forecasting for …