The medical field is growing at a rapid pace, with new diseases cropping up daily that require invention of appropriate course of treatment. The heart is a muscular organ which is the size of clenched human fist, and is responsible for blood circulation. Also squeezing the classification model with entire raw features can create a bottleneck to the classification performance. Though, heart/cardiac disease is the name given to diseases affecting the heart in general, there are many diseases which come under this name, including coronary artery diseases (CAD), cardiomyopathy, cardio vascular disease (CVD) and so on depending upon the circulation of blood throughout the body. Hence, this research work initiates a hybrid method named HCFFSC- hierarchical clustering fuzzy features subset classification for identifying appropriate feature subsets related to target class and given to classifier model to enhance the performance of health monitoring and management (HMM).
Top1. Introduction
In medical field, the accuracy of the disease diagnosis plays vital role as it leads to further treatment of the patient. So the prime objective dissertation is to improve the diagnosing accuracy of the medical expert system by,
- •
Employing feature optimization techniques to select most significant feature subset in the medical data.
- •
Constructing various classifier models (two-class) to train and test the clinical data.
- •
Optimizing classifier parameters and fuzzy rules by using single and hybrid optimization techniques.
Big data classification is considered as a critical and challenging problem to be addressed in big data analytics. The action of classifying the data using issues and difficulties opened by the big data controllers is called big data classification (Anifah et al., 2017). Figure 19.1 shows the distinguishing parameters paces associated with the deep learning algorithms and the flow of various phases is demonstrated. The cross validation and early stopping decision methods are applied for solving problems seen in the validation phase (Potghan et al., 2018).
Figure 1.
Big data classification process
Machine learning and data mining techniques have to be customised in an efficient way for ensuring complete capability of gathered data. The lungs are protected by the pleura and the thin fluid act as a lung smoothly which is helpful to expand while breathing. For example, in medical science, astronomy etc. the predictive ability of big data is largely used. The third party resources do almost all computations on private data that lead to threat for user’s privacy Alam et al. (2018).
Predictive analytics answers the query “What could happen?” by understanding and estimating the forthcoming issues using statistical methods and various forecasting methods. It uses techniques like machine learning, statistics and data mining for predicting the future. Prescriptive analytics answers the query “What should we do?” by complicated data received from the descriptive and predictive analyses. This model detects the best alternative to minimize or capitalize marketing, finance and other sectors by optimization methods. For example, one select prescriptive analytics if one wants to make the choice of a perfect way to the carriage of stuff from one industry to another location at a minimal cost (Khang & Rana et al., 2023).
The reminder of paper is organized as follows. Section 19.2, big data medical dataset prediction and its related work, Section 19.3 discussed to Hybrid Hierarchical clustering feature subsets classifier algorithm, section 19.4 presents proposed system and existing systems experimental results comparison. Finally, section fifth provides the concluding remarks and future scope of the work (Khang & Vladimir et al., 2023).
Top2. Literature Review
Tafti et al. (2018) have suggested mentioned that primary hazard components of glaucoma are hoisted IOP applied by watery diversion, family history of glaucoma (genetic), astigmatism or partial blindness, glaucoma in the other eye, retinal separation, injury to the eye, diabetes, pigmentary scattering disorder, slender points, low fundamental circulatory strain, headache migraines or visual headaches, raynaud's disorder, blood thickening, irregular visual field tests, undesirable optic nerve, corneal dystrophy and pseudo shedding.