Heart Disease Prediction using NAFS and Image Processing - Nihal B. S., Mohamed; Gnanavel, S. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
The detection of Heart Disease (HDD) is becoming increasingly important as it is recognised as a leading cause of death worldwide, especially in high-income countries. About 7 million people each year lose their lives to HDD, with men being affected more than women (12%). As a result, several different approaches to disease element analysis have been developed, all with the same goal in mind: to reduce the variation in doctors’ clinical practises, as well as clinical costs and mistakes.Developing a robust and clever data-mining-based clinical decision-support system is the primary goal of this study. In order to efficiently detect and identify cardiac illness, the method developed consists of pre-processing, de-noising, clustering, filtering, segmentation, feature extraction, and classification. Other methods such as the Bilateral filter, Gaussian filtering, the average filter, and the notch filter for filtering linked factors, and the Sobel edge detection for locating specific illnesses, are also available. Background and foreground segmentation of identified disease-affected regions is accomplished with the help of ROI segmentation. Extraction of DWT characteristics typically yields a high-quality, relevant image. The New adaptive neuro-fuzzy system (NAFS) algorithm, which is performance-growing when compared to various existing methodologies, was developed in response to this ongoing need for a more sophisticated kind of algorithm. When used to the estimation of the bushy or fuzzy rule sets method in a neural network, this new adaptive neuro-fuzzy system algorithm is a part of the machine learning methodology that is being used to boost the system’s overall performance.