Bio-Inspired Algorithms for Medical Data Analysis

Introduction

Modern medicine generates almost daily, huge amounts of heterogeneous data. For example, medical data may contain SPECT images, signals like ECG, clinical information like temperature, cholesterol levels, etc., as well as the physician's interpretation. Those who deal with such data understand that there is a widening gap between data collection and data comprehension. Computerized techniques are needed to help humans address this problem. This volume is devoted to the relatively young and growing field of medical data mining and knowledge discovery. As more and more medical procedures employ imaging as a preferred diagnostic tool, there is a need to develop methods for efficient mining in databases of images. Other significant features are security and confidentiality concerns. Moreover, the physician's interpretation of images, signals, or other technical data, is written in unstructured English which is very difficult to mine.

Health care organizations are struggling to find new ways to cut healthcare utilization and costs while improving quality and outcomes. Predictive models that have been developed to predict global utilization for a healthcare organization cannot be used to predict the behavior of individuals. On the other hand, massive amounts of healthcare data are available in databases that can be used for exploring patterns and therefore knowledge discovery. Diversity and complexity of the healthcare data requires attention to the use of statistical methods. By nature, healthcare data are multivariate, making the analysis difficult as well as interesting.

Vast quantities of data are generated through the health care process. While technological advancements in the form of computer-based patient record software and personal computer hardware are making the collection of and access to health care data more manageable, few tools exist to evaluate and analyze this clinical data after it has been captured and stored. Evaluation of stored clinical data may lead to the discovery of trends and patterns hidden within the data that could significantly enhance our understanding of disease progression and management. Techniques are needed to search large quantities of clinical data for these patterns and relationships. Past efforts in this area have been limited primarily to epidemiological studies on administrative and claims databases. These data sources lack the richness of information that is available in databases comprised of actual clinical data.

In this study, we propose application of bio inspired algorithms in one of the major real life issues, medical data analysis. We have applied four bio-inspired algorithms on seven different medical datasets, the organization of the chapter was given as following: next section we give some related works to our study, then we presented the cases studied, where we give idea about the seven datasets used in experiments then we explain the four algorithms applied for classification of those datasets, and we defined measures used for evaluation, after that we discuss the obtained results. Finally, we give major conclusions.