A SURVEY ON MEDICAL DATA MINING TECHNIQUES

Patient Safety: Grantmakers Join The Effort To Reduce Medical Errors

The way we learn has changed as a result of advances in science and technology and the growing influence of information technology on daily life. E-learning is a relatively new concept in educational settings, but it has progressively earned a spot in modern training techniques. Data mining methods are used in many applications, particularly in elearning. Comparing direct communications difficult to track the student involvement in online courses. To overcome the above mentioned issues the existing methods are analyzed various techniques of data mining and optimization methods which can be applied to bring out hidden knowledge from the educational data? In the e-learning work, preprocessing, feature selection and classification process are performed to improve the Kalboard 360 educational dataset accuracy significantly. This study focused on educational data mining (EDM) and employed decision trees, support vector machines (SVM), multilayer perceptrons, and the naive bayesian C4.5 algorithm among other classification techniques to assess the features for the prediction of students’ behaviour and academic achievement. This paper analyzes the advantages and shortcomings of each algorithm applied to data set on the basis of accuracy for various classification techniques like Multilayer Perceptron based ANN algorithm (MP-ANN), Decision Tree (DT) C4.5 and Hybrid Regression model with Multi Label Classification (HR-MLC) approach. The experimental result shows that the HR-MLC algorithm provides better performance in terms of higher accuracy, precision, recall and f-measure rather than the other existing methods.

Risk prediction model estimates event occurrence based on related data. Conventional statistical metrics that utilized primary data generates simple descriptive analysis that often provide insufficient knowledge for decision making. In contrast, data mining techniques that have the capability to find hidden pattern from the secondary data in large databases and create prediction for de- sired output has become a popular approach to develop any risk prediction model. In healthcare particularly, data mining techniques can be applied in disease risk prediction model to provide reliable prediction on the possibility of acquiring the disease based on individual’s clinical and non-clinical data. Due to the increased use of data mining in healthcare, this study aims at identifying the data mining techniques and algorithms that are commonly implemented in studies related to various disease risk prediction model as well as finding the accuracy of the algorithms. The accuracy evaluation consists of various method, but this paper is focusing on overall accuracy which is measured by the total number of correctly predicted output over the total number of prediction. A systematic literature review approach that search across five databases found 170 articles, of which 7 articles were selected in the final process. This review found that most prediction model used classification technique, with a focus on decision tree, neural network, support vector machines, and Naive Bayes algorithms where heart-related disease is commonly studied. Further research can apply similar algorithms to develop risk prediction model for other types of diseases, such as infectious disease prediction.

The online version contains supplementary material available at 10.1007/s40200-021-00884-2.

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

Predictive analytics has emerged as a powerful tool in the field of financial engineering, enabling organizations to forecast trends, optimize strategies, and mitigate risks. Data mining techniques play a crucial role in extracting meaningful patterns from large volumes of financial datasets, contributing to the development of predictive modeling techniques. The purpose of this paper is to explore the application of various data mining techniques in predictive analytics within the financial sector, with a focus on their effectiveness in risk management, fraud detection, and portfolio optimization. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are increasingly being employed to model complex financial systems and enhance predictive accuracy. Additionally, techniques like clustering, association rule mining, and time series analysis are used to uncover hidden relationships and temporal patterns in financial datasets. Despite the significant advancements in these techniques, challenges remain in terms of data quality, computational complexity, and interpretability of results. This paper addresses these challenges and provides a comprehensive review of the most widely used data mining methods in financial engineering. Furthermore, it discusses the potential for future advancements in data mining algorithms and their integration with other emerging technologies like artificial intelligence and big data analytics. The findings indicate that while data mining offers significant potential in financial analytics, careful consideration must be given to the selection of appropriate techniques and the proper handling of data to ensure reliable predictions. Ultimately, this paper aims to contribute to the ongoing development of robust predictive modeling techniques in financial engineering by emphasizing the importance of data mining as an indispensable tool.

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural networks, support vector machine, and lazy modelling are considered. Results show data mining techniques can considerably help a physician.

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.

According to the World Health Organization (WHO), it has been recorded that up to now more than 150 million people have diabetes, whether they are elderly people, adults, teenagers, men or women. Early knowledge of diabetes can be seen based on data from patients who already have diabetes. The patient's disease data has previously been stored and arranged in a data warehouse or what is commonly referred to as a dataset. Therefore, it is necessary to process the data contained in the dataset. But the use of data mining techniques themselves must be assisted by using the techniques contained in the data mining, namely classification techniques. K-Nearest Neighbor (K-NN) is one of the methods used in the classification technique. In the results of the classification of the level of confidence obtained in the process, it is seen based on the amount of accuracy. However, there are important issues that need special attention. In the dataset used for the classification process, the data collected contains unbalanced class results (balance). The unbalanced data classification process becomes an important problem, this is because it can cause a decrease in performance. Adaboost is a technique in data mining that can be used to increase the level of accuracy in classification methods. The results showed that the adaboost algorithm can help improve classification performance. This can be seen from the increasing level of accuracy obtained from the process carried out before and after using the adaboost algorithm. The results obtained from the research show that the adaboost algorithm can be used properly to help the performance of the K-Nearest Neighbor algorithm for the classification process on diabetes datasets. It can be seen from 5 tests with values of K = 7, 13, 19, 25 and 31 there is an increase in the accuracy results obtained after using the adaboost algorithm.

Prediction of post-COVID chronic fatigue syndrome using data mining and machine learning techniques in Isfahan COVID cohort study.

Data mining (DM) is a most popular knowledge acquisition method for knowledge discovery. Classification is one of the data mining (machining learning) technique that maps the data into the predefined class and group's. It is used to predict group membership for data instance. There are many areas that adapt Data Mining techniques such as medical, marketing, telecommunications, and stock, health care and so on. This paper presents the various classification techniques including decision tree, Support vector Machine, Nearest Neighbor etc. This survey provides a comparative Analysis of various classification algorithms.

This paper proposes a data mining based sleep wave and sleep stage classification scheme. Sleep wave classification have a greater use in diagnosis of health conditions. Classification of sleep wave and sleep stage may be put into use in clinics where there is a lack of specialist physicians. Various data mining techniques used in this work are Artificial Neural Network (ANN), Support Vector Machines (SVM), Adaptive neuro-fuzzy inference system (ANFIS), k-nearest neighbor (kNN), naive Bayes classifier (NBC), Linear/Quadratic discriminate analysis (LDA/QDA) and Decision tree (DT). The dataset was obtained from physionet sleep EDF dataset repository. The data set includes horizontal EOG, submental-EMG and EEG signals which are inputs to the data mining based network. Output of data mining based network composed of different classes corresponds to a certain sleep wave like alpha, beta, delta and theta and sleep stages like Awake (Stage 0), Stage I, II, III, IV and REM sleep. Accuracy of the classification is 99% for sleep wave and sleep stage. Since DT has the highest accuracy and it is easy to use, it is chosen for sleep wave classification.

Medication errors: What is the pharmacist’s role?

Medical datasets have attracted the research community for possible analysis and suitable prediction, which helps the human to take proper precautions in preventing future diseases. To perform related operations, data mining techniques have been widely used in developing decision support systems for disease prediction through a set of medical datasets. This work proposes a new predictive model for disease prediction using pre-processing techniques for various disease datasets. The proposed model not only analyses the datasets also improves the performance by using ensemble methods. To process the datasets, pre-processing techniques such as discretization, resampling, principal component, and decision tree have been used. To classify the datasets, classification techniques such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF) have been used. The algorithms are applied with 10 fold validation technique. A predictive analysis has also been performed on various disease datasets, where every dataset results in significant improvement for various performance measures. We perform a predictive analysis on the datasets such as CKD (Chronic Kidney Disease), Cardiovascular Disease (CVD) or heart, Diabetes, Hepatitis disease, Cancer disease and ILPD (Indian Liver Patient disease). Experimental results show that the proposed predictive model outperforms in terms of better accuracy.

By Using data mining techniques, much study has been undertaken to derive information from medical data, which may enhance the curing ability. This study has been compared four automated methods for blood diseases detection using a different method for attributes reduction methods: PCA (Principal Component Analysis), ICA (Independent Component Analysis), original data set, and four algorithms, which are Naive Bayes, K-Nearest Neighbor, Decision Tree, and SVM (Support Vector Machine) for classification. The results show that after applying the PCA technique, the accuracy is: 0.98, 1.0, 0.79, and 0.78 for Naive Bayes, Decision Tree, SVM, and KNN and after applying the ICA technique, the accuracy is 0.76,.76, 0.76, and 0.74 for Naive Bayes, Decision Tree, SVM, and KNN, while the accuracy after applying on the original data set is: 0.98, 1.0, 0.78, and 0.75 for Naive Bayes, Decision Tree, SVM, and KNN. The results appeared that when applying decision tree on the original data set and after applying the PCA method, have been gotten the highest accuracy which is 100 %.