TL;DR
A stacking framework that uses harmony search optimization (HSO) to predict early cardiac disease in the course of the disease diagnosis process and the overall accuracy attained is 87%, multilayer stacking is 89%, and the stacking model employing HSO is 92%.
The proposed approach uses a Quantum Neural Network for machine learning for machine learning in an intelligent Cardiovascular Disease (CVD) prediction system that has the highest sensitivity and specificity in the current literature, matching exact expert opinions.
The study's findings revealed that the benchmarking and selection issues associated with heart disease diagnosis models can be effectively addressed by combining Entropy and TOPSIS-VIKOR.
The proposed approach shows that simple and interpretable ensemble models can provide accurate heart disease risk predictions and is combined to improve the transparency and clinical trust.
This research highlights the strengths and limitations of each approach, offering insights into which models may be more effective for similar healthcare applications, and suggests that even greater accuracy may be achieved by exploring more advanced techniques in the future.
Liver disease is a serious health problem prevalent in the world. Early diagnosis is very important for enhancing quality of patient's health and reducing mortality. Traditional diagnostic techniques tend to take quite long time, be expensive and rely heavily on the skill of the medical personnel. That's why intelligen...
This systematic review examines contemporary machine learning methods and explainable AI procedures engaged prediction of heart diseases and classification and applies explainability AI heart disease prediction models that will ease the process and also make the upcoming system with better progress and more trustworthy...
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