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This study performs an Affinity Analysis ondiagnosis and prescription data in order to discover cooccurrencerelationships among diagnosis and pharmaceuticalactive ingredients prescribed to different patient groups. Theanalysis data collected during consecutive visits of 4,473 patients in a 3 years period, focused on patients suffering byhypertension and/or hypercholesterolemia and appliedassociation...
The paper explains an approach that tries to predict the gestational diabetes of the patient using association rule mining. We address the basic risk factors in Gestational Diabetes Mellitus of pregnant ladies leading to abnormality in delivery and post and pre-delivery conditions. The existing approaches deal with social economy conditions of the GEM patients. Medical examinations records can be...
Enormous data mining techniques were used for disease prediction among which only a few have employed feature selection. The prediction knowledge for disease diagnosis highly depends on the subjective knowledge of the experts. Developing a disease prediction model in time can help us to overcome the medical distress. In this paper, three feature selection strategies namely, HS, MS and TS are devised...
Healthcare in simplest form is all about diagnosis and prevention of disease or treatment of any injury by a medical practitioner. It plays an important role in providing quality life for the society. The concern is how to provide better service with less expensive therapeutically equivalent alternatives. Machine Learning techniques (ML) help in achieving this goal. Healthcare has various categories...
Diabetes is a chronic disease that requires continuous treatment throughout lifespan and increased risk opportunity of developing a number of serious health problems, which are high treatment cost. Admitted diabetes inpatients should receive the appropriate treatment in order to reduce rating of severe complications and premature death. This paper aims to develop the classification model for diabetic...
We present an automated disease term classification model using machine learning techniques that classifies a medical term to a specific disease class. We work on five particular diseases: Cancer, AIDS, Arthritis, Diabetes and heart related ailments. We identify and classify medical terms like drug names, symptoms, abbreviations, disease names, tests, etc., into their specific diseases classes. The...
In the era of big data, the Privacy Preserving Data Mining (PPDM) technology is increasingly important. The aim is to dig out the hidden, previously unknown and potentially useful knowledge or pattern under the premise of protecting sensitive data. In this paper, we summarize the existing PPDM technology as well as its advantages and disadvantages. We combine distributed randomization with the K-anonymity...
Data Mining is the process of discovering interesting patterns and knowledge from large amounts of data. One of the most important techniques of Data Mining is classification which is used for prediction purposes. In this paper, we present a novel classifier for classification in the field of Medical Data Mining. The idea is to apply the Adaptive Classifier on the sample medical dataset, and compare...
Data reduction is a process of reducing the datasets in volume, almost used in all real time applications. Although there are several techniques available, many researchers have used K-Means clustering in reducing the datasets. In this paper, three different methods were used to replace missing values with mean, median and a predicted score; the cleaned datasets were reduced using K-Means clustering...
The precise diagnosis of patient profiles into categories, such as presence or absence of a particular disease along with its level of severity, remains to be a crucial challenge in biomedical field. This process is realized by the performance of the classifier by using a supervised training set with labeled samples. Then based on the result obtained, the classifier is allowed to predict the labels...
In our country diagnosis of a disease are done mostly by expertise and experienced doctors, but still there are cases of wrong diagnosis and treatment. [1] Patient have to undergo various test which are very costly and sometimes all of them are not required so in this way it will hugely increase the bill of a patient unnecessarily. In such cases our proposed work will be very helpful. The aim of this...
This paper provides a survey of data mining methods that have been commonly applied to real-world TCM clinical data in recent years, and sets forth the requirements of data mining on real-world TCM clinical diagnosis and treatment data, in order to provide reference for better analyzing the syndrome differentiation and treatment principle hidden in the massive TCM clinical data in the future.
Traditional Chinese Medicine (TCM) is a clinical medicine, which focuses on human physiology, pathology, diagnosis and treatment of diseases. Numerous clinical practice and theory research in the TCM field have accumulated huge amount of data. These data include TCM basic databases, TCM literature, as well as a large number of databases or data warehouse on TCM clinical diagnoses and treatment. More...
Currently data mining techniques and health/medical informatics are still new. Data mining researchers start paying more attention on these matters. Association Rule is one of important methods in data mining. By discovering data association, new useful information can be obtained. In this paper, a researcher has presented a basic method of discovering an association of diabetes mellitus with complication...
Factorizing interleaved event sequences, such as those found in electronic medical records, into simpler processes can yield new insights from large datasets.
Medical data are an ever-growing source of information generated from the hospitals in the form of patient records. When mined properly, the information hidden in these records is a huge resource bank for medical research. As of now, these data are mostly used only for clinical work. These data often contain hidden patterns and relationships, which can lead to better diagnosis, better medicines, better...
The amount of Medical data recorded in hospitals and its significance as an ever-growing source of information has been long known and proven. Though the importance of the information hidden in these records has never been doubted, this data has mostly been used only for clinical purposes. Only recently has this been properly mined for valuable information to be used for research and to develop systems...
This paper presents an approach to support the solution of some of the current public health issues in Colombia. This paper tackles two problems in the healthcare sector. The first verifies the proper provision of health services. The second defines the epidemiological profile of the critical patients to analyze the elapsed time between the detection of their diseases and their evolution to a chronic...
The discovery of knowledge from medical databases is important in order to make effective medical diagnosis. The aim of data mining is to extract knowledge from information stored in database and generate clear and understandable description of patterns. In this study, decision tree method was used to predict patients with developing diabetes. The dataset used is the Pima Indians Diabetes Data Set,...
This research uses association rule generation and classification techniques to support decision making, by considering a data set of diabetes type 1 & type 2 patients. There are advanced and reliable data mining techniques which leads to the discovery of unseen and useful information. The main focus of this research is to identify the yet undiscovered decision factors of diabetes which increases...
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