Garment Categorization Using Data Mining Techniques

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

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.

Diabetes is the common chronic disease and a major health challenge in all population. Gestational diabetes mellitus (GDM) is a type of diabetes developed in women at the time of pregnancy. We present a Data mining (DM) approach to identify the risk factors of Gestational diabetes mellitus (GDM) using different data mining techniques. Dataset used for analysis contains the details of the pregnant women admitted the local hospital of Mysuru, India. The data mining techniques used are k-means clustering, J48 Decision Tree, Random-Forest and Naive-Bayes classifier. Classification accuracy is enhanced by using feature subset selection wrapper approach. Data imbalanced problem is handled by using Synthetic Minority Over-sampling Technique (SMOTE). The performances of the algorithms have been measured and compared in terms of Accuracy.

Education can be utilized as a tool to face many problems, overcome many hurdles in life. The knowledge obtained from education helps to enhance opportunities in one's employment development. To extract useful information from the knowledge obtained, Educational Data Mining is widely used. Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution (school) and can be used to discover a unique pattern of students' academic performance and behavior. The present work intends to enhance students' academic performance in secondary school using data mining techniques. Real data was collected using school reports and questionnaire method by the Portugal school which has been used in this paper. Naive Bayesian algorithm can be easily implemented to predict the students' academic performance and behavior. Classification of students into two classes, pass and fail, involves training phase and testing phase. In training phase, Naive Bayes classifier is built and in the testing phase, Naive Bayes classifier is used to make the prediction. The accuracy of the classifier is calculated using WEKA tool in which confusion matrix is generated. The accuracy of the classifier obtained is 87% which can be further improved by the selection of appropriate attributes. Developing the classification algorithms in this way helps to obtain a more efficient student performance predictor tool using other data mining algorithms and it also helps to improve the quality of education in an educational institution.

Introduction: Prostate cancer is one of the leading causes of death in men, and the early detection of this disease can be a significant factor in controlling and managing it. Applying data mining techniques can lead to the extraction of hidden knowledge from a huge amount of data and can help diagnose this disease by physicians. This study aims to determine the algorithm with the best performance to diagnose prostate cancer.Methods: In this study, nine data mining techniques, including Support Vector Machine, Decision Tree, Naive Bayes, K-Nearest Neighbors, Neural Network, Random Forest, Deep Learning, Auto-MLP, and Rule Induction algorithms, were used to extract hidden patterns from prostate cancer data. In this study, the data of 100 patients, which included eight characteristics, were used, and the RapidMiner Studio environment was employed for modeling. To compare the performance of the mentioned approaches used in this study to diagnose prostate cancer, accuracy, recall, precision, AUC, sensitivity, and specificity were calculated and reported for all techniques.Results: The results of this study showed that the accuracy of the applied algorithms was between 77% and 84%. Using different criteria to evaluate the techniques used showed that the two algorithms K-Nearest Neighbors and Neural Network, had better performance and accuracy (84%) than other methods. The sensitivity in these two algorithms was 80% for Neural Networks and 85% for K-Nearest Neighbors, respectively.Conclusion: The usage of different data mining techniques can lead to the discovery of hidden patterns among an enormous amount of data related to prostate cancer, and as a result, it leads to the early diagnosis of this disease and saves the subsequent costs.

Data mining has gained immense popularity in various fields of medical, education and industry as well. Data mining is a process of predicting the result and extraction of useful information from huge dataset. In this paper, we have surveyed various data mining techniques. Further, performance of various data mining techniques, namely decision tree, random forest, naive Bayes, AdaBoost, multilayer perception neural network, radial basis function, sequential minimal optimization and decision stump, have been evaluated using UCI communities and crime dataset for classifying crime in US states. On the basis of results obtained, we found that the decision tree outperforms with 96.4% accuracy and minimal false-positive rate.

Data mining and machine learning techniques can be applied to computer system design to aid in optimizing design decisions, improving system runtime performance. Data mining techniques have been investigated in the context of branch prediction. Specifically, a comparison of traditional branch predictor performance has been made to data mining algorithms. Additionally, the possiblity of whether additional features available within the architectural state might serve to further improve branch prediction has been evaluated. Results show that data mining techniques indicate potential for improved branch prediction, especially when register file contents are included as a feature set.

This study aimed to identify patients with the highest risk of periodontal disease (PD), and to provide recommendations for the effective use and application of data mining (DM) techniques when establishing evidence-based dental-care policies for vulnerable groups at a high risk of PD. This study used the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology to construct DM models based on data acquired from the fifth and sixth Korea National Health and Nutrition Examination Surveys (2000-2015). We analyzed the sociodemographic and comorbidity variables that influence PD by applying the popular DM techniques of decision-tree, neural-network, and regression models, and also attempted to improve the predictive power and reliability by comparing the results obtained by these three models. Our comparisons of the three DM algorithms confirmed that the average squared error, misclassification rate, receiver operating characteristic index, Gini coefficient, and Kolmogorov-Smirnov test results were the most appropriate for the decision-tree model. The analysis of the decision-tree model revealed that age and smoking status exert major effects on the risk of PD, and that stress and education level exert effects in rural areas, whereas education level, sex, hyperlipidemia, and alcohol intake exert effects in urban areas. We demonstrated that the decision-tree model is an effective DM technique for identifying the complex risk factors for PD. These results are expected to be helpful in improving the equality and efficacy of dental-care policies for vulnerable groups at a high risk of PD.

The objective of data mining is to find the useful information from the huge amounts of data. Many researchers have been proposed the different algorithms to find the useful patterns but one of the most important drawbacks they have found that data mining techniques works for single data table. This technique is known as traditional data mining technique. In this era almost all data available in the form of relational database which have multiple tables and their relationships. The new data mining technique has emerged as an alternative for describing structured data such as relational data base, since they allow applying data mining in multiple tables directly, which is known as Multi Relational data mining. To avoid the more number joining operations as well as the semantic losses the researchers bound to use Multi Relational Data Mining approaches. In this paper MRDM focuses multi relational association rule, Multi relational decision tree construction, Inductive logic program (ILP) as well three statistical approaches. We emphasize each MR-Classification approach as well as their characteristics, comparisons as per the statistical values and finally found the most research challenging problems in MRDM.

The application of classification models in the credit rating of banking customers has been investigated in the present paper. Credit rating is one of the main applications of data mining in the banking industry. The customers' creditworthiness can be evaluated through credit ratings. The data related to banking customers is very huge, and various classification techniques can be used to explore the hidden pattern and knowledge in data set through data mining. Several studies have been performed on the use of data mining and classification techniques in the credit rating of banking customers. After preparing and preprocessing the data using the C5 decision tree algorithm in this paper, the classification model has been constructed and the credit rating of banking customers has been performed. A set of credit rating data has been used in this regard for teaching and testing the model. The results show that the developed model ranks banking customers with high accuracy by using decision tree making classification algorithms. The proposed classification model can also be used for a credit rating of new banking customers.

The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management

Controlled clinical trials are studies done by scientists on a concrete domain with the objective of execute a prospective analysis that attempts to compare the effect and value of one or more interventions in humans under specific conditions. Our case study is focused on applying artificial intelligence techniques to support scientists working on bio-nutritional trials, which rely on the study of the effect of consuming food with biomarkers on human health. This work is developed under the HENUFOOD2project. In order to simplify the domain, two trial stages are considered: the trial experimental execution and the trial data analysis. It is after the trial has been executed and all the information collected, when we try to discover non obvious knowledge among the trial data and support the scientists on their everyday analytical tasks. On the one hand, this analysis includes generic statistical manipulation and data discovery empowered by data mining techniques. On the other hand, a scientist support engine has been developed in order to allow non data manipulation and analysis experts to access the information and ask for new analysis processes. The approach, thanks to a user friendly interface enabled with query answering techniques, is able to interact with the user in a conversational mode and run analytical or data discovery processes, do a semantic search on previous indexed works or, finally, infer new rules to feed the expert system ontology. The expert system is not fully covered on this paper, as it will focus on the explanation of the usage of data mining and query answering techniques on that bio-nutritional focused expert system.

The application of Data Mining (DM) techniques for DQ, often called Data Quality Mining (DQM), offers a wide range of possibilities for DQ assessment. The goal of this work is to propose a mechanism for data currency assessment using statistics and DM techniques. The proposed approach consists on estimating the validity period for the entities using a training set and then evaluating the probability of currency of the last known data value for each entity. The proposed scheme helps in two ways to lead to an always up-to-date database: it can warn if a certain data value is becoming obsolete, and it can inform the data manager about the best frequency for updating data.

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.

<p>In data mining, we are extracting data from historical knowledge and create data sets. Data mining is similar to discover data knowledge from data warehouse and converts it into understandable format. In India, furthersocieties are listening aboutthe medical fitness and health opinion problems. In China, more than 200 thousand people die due to medication errors and 100 thousand in USA .More than 45% medication errors are caused by physicians as specialists write the prescription giving to their practices which are quite limited. In India, at every moment to take care of health and maintain medicine records is very tedious task in pharmaceutical industry, medical ministry and health industry</p> <p><a href="https://journalnx.com/journal-article/20150678">https://journalnx.com/journal-article/20150678</a></p>