[HTML][HTML] Enhancing heart disease prediction accuracy through machine learning techniques and optimization
N Chandrasekhar, S Peddakrishna - Processes, 2023 - mdpi.com
In the medical domain, early identification of cardiovascular issues poses a significant
challenge. This study enhances heart disease prediction accuracy using machine learning …
challenge. This study enhances heart disease prediction accuracy using machine learning …
[HTML][HTML] Thyroid disease prediction using selective features and machine learning techniques
Simple Summary The study presents a thyroid disease prediction approach which utilizes
random forest-based features to obtain high accuracy. The approach can obtain a 0.99 …
random forest-based features to obtain high accuracy. The approach can obtain a 0.99 …
[HTML][HTML] Integrated approach using deep neural network and CBR for detecting severity of coronary artery disease
V Sapra, L Sapra, A Bhardwaj, S Bharany… - Alexandria Engineering …, 2023 - Elsevier
Despite major diagnostic progress and treatment progress, cardiovascular diseases (CVD)
continue to be the world's leading cause of disease and mortality. Artificial intelligence …
continue to be the world's leading cause of disease and mortality. Artificial intelligence …
[HTML][HTML] Combining CNN features with voting classifiers for optimizing performance of brain tumor classification
Simple Summary This study presents a hybrid model for brain tumor detection. Contrary to
manual featur extraction, features extracted from a convolutional neural network are used to …
manual featur extraction, features extracted from a convolutional neural network are used to …
[HTML][HTML] Automated battery making fault classification using over-sampled image data CNN features
NU Din, L Zhang, Y Yang - Sensors, 2023 - mdpi.com
Due to the tremendous expectations placed on batteries to produce a reliable and secure
product, fault detection has become a critical part of the manufacturing process. Manually, it …
product, fault detection has become a critical part of the manufacturing process. Manually, it …
Bee detection in bee hives using selective features from acoustic data
Honeybees, a key pollinator of the world's most cultivated crops, are experiencing colony
collapses due to a variety of factors. The existence of honeybees and queens is critical to the …
collapses due to a variety of factors. The existence of honeybees and queens is critical to the …
[HTML][HTML] Analyzing influence of COVID-19 on crypto & financial markets and sentiment analysis using deep ensemble model
PB Washington, P Gali, F Rustam, I Ashraf - Plos one, 2023 - journals.plos.org
COVID-19 affected the world's economy severely and increased the inflation rate in both
developed and developing countries. COVID-19 also affected the financial markets and …
developed and developing countries. COVID-19 also affected the financial markets and …
A Review of Machine Learning's Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges
Cardiovascular disease is the leading cause of global mortality and responsible for millions
of deaths annually. The mortality rate and overall consequences of cardiac disease can be …
of deaths annually. The mortality rate and overall consequences of cardiac disease can be …
[HTML][HTML] Heart failure detection using instance quantum circuit approach and traditional predictive analysis
The earlier prediction of heart diseases and appropriate treatment are important for
preventing cardiac failure complications and reducing the mortality rate. The traditional …
preventing cardiac failure complications and reducing the mortality rate. The traditional …
[HTML][HTML] Detecting thyroid disease using optimized machine learning model based on differential evolution
Thyroid disease has been on the rise during the past few years. Owing to its importance in
metabolism, early detection of thyroid disease is a task of critical importance. Despite …
metabolism, early detection of thyroid disease is a task of critical importance. Despite …