Data Mining for Health Care Industry: A Practical Machine Learning Tool

Zeinab Daliri
Healthcare generates mountains of administrative data about patients, hospitals, claims, etc. Clinical trials, electronic patient records and computer supported disease management will increasingly produce mountains of clinical data. Data mining products are designed to take this one stage further. It brings the facility to discover patterns and correlation hidden within the data repository and assists professionals to uncover these patterns and put them to work. Therefore, decisions rest with
more » ... ealthcare professionals, not the information system experts. The key to successful data mining is to first define the business or clinical problem to be solved. Thus knowledge can automatically be obtained by the use of machine learning techniques in the hands of healthcare decision-makers. Data mining applications can greatly benefits most areas involved in health care industry. The huge amounts of data generated by healthcare transactions are too complex and large to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. This paper explores data mining applications in major areas such as evaluation of treatment effectiveness, and management of healthcare. It is an essential to interpret the correct diagnosis of patient with the help of clinical examination and investigations. Computer information based decision support systems can play an important role in accurate diagnosis and cost effective treatment. Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. Our healthcare sector daily collects a huge data including clinical examination, vital parameters, investigation reports, treatment follow-up and drug decisions etc. Data mining is concerned with data processing, identifying patterns and trends in information. Increasing computer data analysis awareness, better online education availability and developing an integrated learning approach among medical professionals will definitely helpful for accurate diagnosis and effective treatment management plan. Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. These tools can include statistical models, mathematical algorithms, and machine learning methods-algorithms that improve their performance automatically through experience, such as neural networks or decision trees. Consequently, data mining consists of more than collecting and managing data, it also includes analysis and prediction. Data mining can be performed on data represented in quantitative, textual, or multimedia forms. Data mining applications can use a variety of parameters to examine the data. They include association, sequence or path analysis, classification, clustering, and forecasting. Data mining can enable clinicians to find valuable new patterns in data, leading to potential improvement of resource utilization and patient health. The proposed research of data mining will focus on evidence-based patterns from previous patient treatment. The absence of automated