Comparative Analysis : Classification Algorithms

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.

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

Clinical Decision support system (CDS) is one of the approach for evidence-based information to clinician at the point of care. The CDS brings about a provision for clinical knowledge clubbed with patient specific information. This enhances patient care. In most of the cases, the rules that are used for CDSS include “If conditions then criticism”. The other includes Bayesian reasoning, infobuttons etc.

Yes, for me also this is one of the most informative class. I enjoy doing all the assignment because all of them were informative and we can use our own creativity. Putting voice recorder was little bit challenging but the option to put notes was very helpful. Overall i enjoy this class and learning many thing that is needed for my major.

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

In healthcare organization data mining plays the most leading role in the research area. Data mining plays a vital role in various fields of technology. In healthcare industry we gather more information regarding patients, diseases, hospital resource, electronic patient’s records, diagnosis methods, etc., by using health care in data mining it is easy to classify or group the patients having the same disease so that it helps to treat them effectively. In this paper I have reviewed about data mining application in health care and data mining challenges in health care.

But due to partially complete medical data sets, accuracy often decreases, results in more number of misclassification that can lead to harmful complications. An accurate prediction and diagnostic of a disease becomes a challenging research problem for many researchers. Therefore, aimed to improve the diagnosis accuracy we have proposed a new methodology, based on novel preprocessing techniques, and K-nearest neighbor classifier. The effectiveness of proposed methodology is validated with the help of various quantitative metrics and a comparative analysis, with previous reported studies using the same UCI dataset focusing on pimadiabetes disease diagnosis.

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.

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

All patients were diagnosed with a stage 2, 3, or 4 idiopathic MH according to the Gass classification system(17) and underwent a comprehensive ophthalmologic examination before and 1, 3, and 6 months postoperatively, including measurement of the best-corrected VA (BCVA), binocular indirect ophthalmoscopy, and non-contact lens slit-lamp biomicroscopy.

Abstract: In this term paper study discuss about the important and aim of the study in this article assist to know how managing electronic records at public agencies in Malaysia using function-based classification. Electronic records ensures that the information of organization has the records it needs when they are needed. It is information captured through electronic means, and which may or may not have a paper record to back it up. According in this article, the Department of Syariah Judiciary Malaysia is the first investigation case that serve the function to make the snapshot that can be used at certain time for the future. The process of classifying records for retrieval, appraisal and disposal more easily when used the function based classification model. Development of function based records classification from context level to decomposed diagram using IDEF0 tool by experts academically and professionally. The case study was taken by Malaysian legal records. Function-based classification that show the context for records rather than content other than support proactive management of records.

Abstract—Taking care and maintenance of a healthy popula- tion is the strategy of each country. It is difficult to discover all the side effects for any marketed drug so ADEs are reported by Healthcare providers and Consumers. The era of social media generates an enormous amount of data for analysis. ADEs are extracted from these sources and stored into HDFS in order to perform drug related classification using Supervised Learning algorithms. ADEs can then be validated by data analytics. The proposed system consists of combination of knowledge base and data processing engine, a simple interface for system-user interaction Which enables user to learn system quickly and use it efficiently. Experimental analysis suggests that using in-memory

In this system, the performance of CBR Algorithm will be boosted based on MapReduce approach and to detect diabetes of a particular patient with improved CBR algorithm by using Apache Hadoop framework.

Machine learning has been gaining popularity in healthcare because of its ability to use existing mathematical models and apply them to new instances of an established concept in other data. This ability to automatically identify patterns in data is one of the major reasons for the potential of machine learning in healthcare settings—as well as its ability to fill in the gaps of expert knowledge, adjust for exceptions, and efficiently handle massive amounts of data [1].