Comparative Analysis : Classification Algorithms

Data mining is the process of releasing concealed information from a large set of database and it can help researchers gain both narrative and deep insights of exceptional understanding of large biomedical datasets. Data mining can exhibit new biomedical and healthcare knowledge for clinical decision making. Medical assessment is very important but complicated problem that should be performed efficiently and accurately. The goal of this paper is to discuss the research contributions of data mining to solve the complex problem of Medical diagnosis prediction. This paper also reviews the various techniques along with their pros and cons. Among various data mining techniques, evaluation of classification is widely adopted for supporting medical diagnostic decisions.

Identification of credible and authentic information has become very difficult. Some bloggers may post false information just for financial benefits. Data mining has created an opportunity for the health professional to examine and identify quality information. Big data plays the role to avail quality database (Kudyda, 2016). The explosion of data can now be controlled through data mining. Additionally, big data accommodate the large volume of data in different formats. The information in big data is after that e complimented through animation texts and videos (Crockett, & Eliason, 2016). The data is also organized based on the content. Big data and data mining create quality and consistent information, which is examined by professional with authority on the subject.

The ability to predict patient’s condition is very important in hospital settings. Patients’ condition is based upon diverse factors like personal, psychological and other health problems. Different data mining algorithms are used to achieve the goal of predicting the patient’s condition.

Every day the surgical interventions are associated with medicine, and the area of critical care medicine is no exception. The goal of this work is to assist health professionals in predicting these interventions. Thus, when the Data Mining techniques are well applied it is possible, with the help of medical knowledge, to predict whether a particular patient should or not should be re-operated upon the same problem. In this study, some aspects, such as heart disease and age, and some data classes were built to improve the models created. In addition, several scenarios were created, with the objective can predict the resurgery patients. Regarding the primary objective, the resurgery patients’ prediction, the most important metric is the sensitivity,

Knowledge attained wth the use of data mining techniques can be used to make innovative and successful decisions that will increase the success rate of health care sector and the health of patients. In this paper, the study of classification algorithms in data mining techniques and its applications are discussed. The popular classification algorithms used in healthcare domain are explained in detail. The open source data mining tools are discussed. The applications of healthcare sector using data mining techniques are studied. With the future development of information communication technologies, data mining will attain its full potential in the discovery of knowledge hidden in the health care organizations and medical

Sequential patterns are a new application that has been proposed to help doctors and diabetic patients. There is one main advantage to the sequential patterns. The article says the advantage is the transparency of the model. These patterns can be easily understood by physicians. The sequential patterns were used to tie information together and make patterns between different hospitals. Mining diabetes data is used with sequential patterns. The patterns

Orem’s theory of self care deficit specifies when nursing is needed. “Nursing is required when an adult (or in the case of a dependent, the parent) is incapable or limited in the provision of continuous effective self care” (Current Nursing, 2010, para. 16). Orem’s created five methods of helping; which are acting for and doing for others, guiding others, supporting one another, providing an environment that promotes personal development, and teaching one another.

ARTIFICIAL INTELLIGENCE (AI) tools and techniques can aid in the diagnosis of disease states and assessment of treatment outcomes, so AI can be used by a decision support system as pattern recognition to analyze healthcare data and generate a representation of knowledge and make a decision support.

Data mining as it has been established is simply a way of getting hidden knowledge or information from data, the technique(s) employed search for concurrency, relationships, and outliers in this data that they present as knowledge [4]. This knowledge can then be used in different applications. Prediction is one of the ways that the hidden knowledge gotten from the data can be used. The main aim is to use a large number of past values to consider probable future [9]. Heart disease is the common term for a variety of diseases, conditions, and disorders that affect the heart and the blood vessels [6]. Symptoms of heart disease vary depending on the type of heart disease. Congenital heart disease suggests to a problem with the heart 's structure and function due to abnormal heart development before birth. Congestive heart failure is when the heart does not pump adequate blood to the other organs in the body [8]. Coronary heart disease or in its medical term Ischemic heart disease is the most frequent type of heart problem [24]. Coronary heart disease is a term that refers to damage to the heart that happens because its blood supply is decreased; it leads to fatty deposits build up on the linings of the blood vessels that provide the heart muscles with blood, resulting in heart failure. Heart disease prediction using data mining is one of the most interesting and challenging tasks. Shortage of specialists and high wrongly diagnosed

However, in our age we have resources that haven’t always been available to solve these problems. Electronic health care records, the internet, mobile phones and apps are a few of the many technologies that contribute to the copious amount of data we have access to today. This large amount of data can be overwhelming. While it once seemed impossible to sort through and extract meaningful patterns from these enormous amounts of data, automated data processing techniques have significantly advanced.

However, after extracting the information from a large database, the data are analyzed and summarized into useful information. This process of analyzing and summarizing the extracted data is known as Data Mining (Maimom & Rokach, 2007). In fact, data mining is one of the important steps of KDD process that infer algorithms, explore data, develop model, and discover previous patterns (Maimom & Rokach). Hence, due to the accessibility and abundance of data, knowledge discovery and data mining have become considerably important in the healthcare industry (Maimom & Rokach).

Abstract— Data Mining extracts useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases that includes data preprocessing, classification, clustering, association rules and sequential patterns. This paper analyses the performance of two classification techniques such as Bayesian

As with the development of the IT technologies , the amount of cumulative data is also Growing. It has resulted large amount of data stock in databases therefore the Data mining comes into model to explore and analyses the databases to extract the interesting and previously obscure patterns and rules well-known as association rule mining It was first introduced in 1993.

Data mining is the process of discovering patterns, trends, correlations from large amounts of data stored electronically in repositories, using statistical methods, mathematical formulas, and pattern recognition technologies (Sharma n.d.). The main idea is to analyze data from different perspectives and discover useful trends, patterns and associations. As discussed in the previous chapter, the healthcare organizations are producing massive amounts of electronic medical records, which are impossible to process using traditional technologies (e.g., Microsoft excel). Therefore data mining is becoming very popular in this field as it can be used to identify the presence of chronic disease, detect the cause of the disease, analyze the effectiveness of treatment methods, predict different medical events, identify the side effects of the drugs, and so on. Kidney diseases such as CKD or AKI require immediate detection and medical attention based on the patient’s clinical condition, medical history, medication history and some demographical factors. From the literature survey, we discovered a good number of studies and tools that used data mining methods such as clustering, association, and classification to improve the decision-making ability of the healthcare providers regarding kidney disease. In the subsequence sections in this chapter, we present an overview of the data mining methods and discuss how they have been used in existing literature.

In medical fields, clinical databases are recently becoming increasingly large so that more experts are needed to deal the data. Since the majority of the medical data sets are non-linear, it is difficult for people to analysis and classify a large amount of them. Therefore if there are some specific algorithms which could automatically handle numerous data, it could efficiently reduce the human