Systematic Analysis of Applied Data Mining Based Optimization Algorithms in Clinical Attribute Extraction and Classification for Diagnosis of Cardiac Patients
This chapter covers the data mining techniques applied to the processing of clinical data to detect cardiovascular diseases. Technology evaluation and rapid development in medical diagnosis have always attracted the researchers to deliver novelty. Chronic diseases such as cancer and cardiac have been under discussion to ease their treatments using computer aided diagnosis (CAD) by optimizing their architectural complexities with better accuracy rate. To design a medical diagnostic system, raw ECG Signals, clinical and laboratory results are utilized to proceed further processing and classification. The significance of an optimized system is to give timely detection with lesser but essential clinical attributes for a patient to ensue surgical or medical follow up. Such appropriate diagnostic systems which can detect abnormalities in clinical data and signals are truly vital and various soft computing techniques based on data mining have been applied. Hybrid approaches derived from data mining algorithms are immensely incorporated for extraction and classification of clinical records to eliminate possible redundancy and missing details which can cause worse overhead issues for the designed systems. It also extends its applications in selection, processing and ranking clinical attributes which are integral components of any medical diagnostic system. Such systems are evaluated by determining the performance measures such as system's accuracy, sensitivity and specificity. Various supervised and unsupervised learning algorithms have been ensemble with feature processing methods to optimize in the best possible manner to detect cardiac abnormalities. This chapter analyzes all the earlier applied approaches for the cardiac disease and highlights the associated inadequacies. It also includes the architectural constraints of developing classification models. Hybrid methodologies combined with requisite clinical extraction and ranking tools to enhance system's efficiency are also discussed. This systematic analysis of recent applied approaches for cardiac disease, aids in the domain of clinical data processing to discuss the present limitations and overcome the forthcoming complexity issues in terms of time and memory. Further, it explains that how efficient techniques for data processing and classification have not been used appropriately by considering their strengths in either phase, which leads to processing overhead and increased false alarms. Overall, the aim of this chapter is to resolve assorted concerns and challenges for designing optimized cardiac diagnostic systems with well tuned architecture.
Applications of Intelligent Optimization in Biology and Medicine