Rule extraction from convolutional neural networks for heart disease prediction.

The accurate prediction of heart disease is crucial in the field of medicine. While convolutional neural networks have shown remarkable precision in heart disease prediction, they are often perceived as opaque models due to their complex internal workings. This paper introduces a novel method, named Extraction of Classification Rules from Convolutional Neural Network (ECRCNN), aimed at extracting rules from convolutional neural networks to enhance interpretability in heart disease prediction.

Early detection of Parkinson disease using stacking ensemble method.

Parkinson's disease (PD) is a common progressive neurodegenerative disorder that occurs due to corrosion of the substantianigra, located in the thalamic region of the human brain, and is responsible for the transmission of neural signals throughout the human body using brain chemical, termed as "dopamine." Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include the presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Hence, sometimes the process of diagnosis may also be affected by human error.