Data Mining Techniques For Prediction Research Articles

Anti-Hypertension Drugs Classes of Prevention and Side Effects Diseases Efficiency Analysis in Association Rule Mining Techniques

Purpose: The Healthcare department, pharmaceutical department, Hospital and Clinical sector find out and explore the diseases of status, where it has been spreaded the communicable and non-communicable diseases among the society. The healthcare department conducting different awareness of programming about the different diseases how is affected people and how is prevented the diseases in society. Accordingly, all the healthcare awareness of information passed through the different media channel, even though high and low blood pressure is pressing public health challenges and it is recognized as the biggest contributor to the global burden of diseases. Presently people health is silently affected by blood pressure low and high level and they unable to recognize that something is amiss, high blood pressure is known as the "silent killer” and If blood pressure is excessively elevated, it may have an impact on organ damage or health issues like coronary arteries, heart valve dysfunction, diabetes, kidney diseases, heart attack and stroke this all are risk factors of blood pressure abnormal status. Hypertension diseases affected the patients need to the best prevention and feature safety. The Doctors, Pharmacist and Nurse are using Anti - hypertension drugs classes of medicines for patients. Which anti-hypertension drug classes of medicine good efficacy for patients and anti-hypertension drugs classes of medicine prevention diseases, side effects diseases knowledge is important for healthcare professional. Machine learning and Data mining knowledge discovery techniques need to understand how different classes of anti-hypertensive drugs might interact with the patient’s and medications. If the healthcare professional has access to a predictive data mining technique model, they could use this to anticipate how the patient’s condition might change over time and adjust the treatment plan proactively. This kind of analytical data mining knowledge can lead to more effective treatment and better patient outcomes. Design/Methodology/Approach: Developing machine learning concept for different anti-hypertension drugs classes of medicine efficiency analysis in hypertension prevention diseases and side effects diseases and Healthcare professional to take right decision for future adjusts treatment plan to the hypertension affected patients. Findings/Result: Orange data mining analytical tool to identify the anti-hypertension drugs classes of medicines efficacy and Doctors can take right decision to better treatment for the patients. Originality/Value: Data mining association rules of support, confidence and lift correlation analysis system helps to identified about the drug of new knowledge efficiency. Paper Type: Analytical research methods applied for analysis the different types of anti-hypertension drugs classes of association correlation efficacy in data mining machine learning system.

Hybridization of fuzzy rough feature selection with ANFIS and turbulent flow of water optimization for managing software defect prediction uncertainty

By anticipating system defect-prone units, software-developing businesses aim to increase the quality of software. Despite the development of numerous Data Mining (DM) and Artificial Intelligence (AI) techniques in the Software Defect Prediction (SDP) field, dealing with the uncertainty of datasets persists due to noise, data distribution, class overlapping, proposed model parameters, and old data. This uncertainty issue has a negative impact on the accuracy of software defect prediction. To overcome this limitation, a model-based hybridization of Ant Colony Optimization-inspired Fuzzy Rough Feature Selection (FRAC) followed by adapting the parameters of Adaptive Neuro-Fuzzy Inference System (ANFIS) with a novel algorithm called Turbulent Flow of Water Optimization (TFWO) is recommended. The proposed model (FRAC+TFWANFIS) performed better than contemporary literature and other optimization algorithms in SDP, such as Ant Colony Optimization (ACO), Differential Evolution (DE), ANFIS, Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Also, the performance of the proposed model is superior to that of other conventional classification techniques such as Naïve Bayes (NB), Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Fuzzy Rough Nearest Neighbor (FRNN), Fuzzy Nearest Neighbor (FNN), Bagging, C4.5, Random Forest (RF), and K-Nearest Neighbor (K-NN). Two datasets, PC3 and PC4, with large dimensions from the OPENML platform are used. The experiments are applied with regard to accuracy, Standard Deviation (SD), Root Mean Square Error (RMSE), Mean Square Error (MSE), and other measurement metrics. The uncertainty issue is addressed by the (FRAC+TFWANFIS) model with accuracy 90.8% and 91.1% for PC3 and PC4, respectively.

Comparative Analysis of Data Mining Techniques for Heart Disease Prediction: A Focus on Neural Networks and Decision Trees

Heart disease is a general term used to describe numerous medical conditions that directly affect the heart and its various components. It is a prevalent health concern in modern times. The focus of this paper is to evaluate different data mining techniques for the prediction of heart disease, which have been introduced in recent years. The findings indicate that neural networks using 15 attributes demonstrate the best performance among all other data mining techniques. Additionally, the analysis concludes that decision trees, with the assistance of genetic algorithms and feature subset selection, also exhibit high accuracy. The study concludes that data mining techniques can effectively predict heart disease and that the choice of technique depends on the specific context of the analysis. The study suggests that decision trees and artificial neural network models are suitable for heart disease prediction. The study also recommends further research to explore the use of other data mining techniques for heart disease prediction.

Analysis of Student Performance Applying Data Mining Techniques in a Virtual Learning Environment

Students' academic performance is a key factor for educational institutions and society, which is an important indicator of the quality of the teaching-learning process and the appropriation of knowledge. Its analysis allows an understanding of the behavior of students and teachers, generating valuable knowledge for making timely academic decisions. In this study, the following phases were carried out: (i) identification of factors that influence the academic performance of engineering university students, (ii) early prediction of academic success, and (iii) identification of use patterns in a virtual learning environment (VLE). The Knowledge Discovery in Databases (KDD) methodology was applied based on predictive and descriptive data mining techniques, using academic and socioeconomic data and interactions (resources and activities) with the VLE. The tools and programming languages ​​used were Pentaho Data Integration for data integration and processing; Jupyter Notebook, Python, and Scikit-Learn for correlation analysis and prediction modeling; and R Studio for the clustering task. The results show that VLE resources such as files, links, and activities such as participation in forums are factors that lead to good academic performance. On the other hand, it was possible to make predictions of academic success (pass or not) with an accuracy greater than 95% and to identify the main patterns of use of the VLE. The group with excellent academic performance (grades 9 to 10) is recognized for using file-type resources and high participation in class and forum activities.

Prediction of ICU Patients' Deterioration Using Machine Learning Techniques.

Assessing vital sign measurements within hospital settings presents a valuable opportunity for data analysis and knowledge extraction. By generating adaptable, personalized prediction models of patient vital signs, these models can yield clinically relevant insights not achievable through population-based models. This study aims to compare several statistical forecasting models to determine their real-life applicability. The primary objectives of this paper are to evaluate whether the following measurements: blood pressure, oxygen saturation, temperature and heart rate can predict deterioration in Intensive Care Unit (ICU) patients. Additionally, we aim to identify which of these measurements contributes most significantly to our prediction. Lastly, we seek to determine the most accurate data mining technique for real-life data applications. This retrospective chart review study utilized data from patients admitted to the ICU at a tertiary hospital between January and December, 2019. Data mining techniques for prediction included logistic regression, support vector machine classifier, k-nearest neighbors (KNN), gradient boosting classifier, and Naive Bayes classifier. A comprehensive comparison of these techniques was performed, focusing on accuracy, precision, recall, and F-measure. To achieve the research objectives, the SelectKBest class was applied to extract the most contributory features for prediction. Blood pressure ranked first with a score of 9.98, followed by respiratory rate, temperature, and heart rate. Analysis of 653 patient records indicated that 129 patients expired, while 542 patients were discharged either to their homes or other facilities. Among the five training models, two demonstrated the highest accuracy in predicting patient deterioration or survival at 88.83% and 84.72%, respectively. The gradient boosting classifier accurately predicted 115 out of 129 expired patients, while the KNN correctly predicted 109 out of 129 expired patients. Machine learning has the potential to enhance clinical deterioration prediction compared to traditional methods. This allows healthcare professionals to implement preventative measures and improve patients' quality of life, ultimately increasing average life expectancy. Although our research focused exclusively on ICU patients, data mining techniques can be applied in various contexts both within and outside the hospital setting.

Comparing the reliability of classical statistics and data mining techniques in unit energy prediction in circular stone cutting

Comparing the reliability of classical statistics and data mining techniques in unit energy prediction in circular stone cutting

Quality enhancement at higher education institutions by early identifying students at risk using data mining

Accurate prediction of students' academic performance is one of the challenges in maintaining quality standards in any Higher Education Institution (H.E.I.). To ensure the quality of teaching and learning, H.E.I.s often employ Self-Assessment Reports (S.A.R.s) in which identifying a student drop-out ratio is important. Hence, it is essential to identify at-risk students in a given academic program. This article aims to identify at-risk students early by proposing a data mining-based predictive framework to improve the student's learning experience and minimize the dropped-out ratio. The academic sub-attributes or indicators in each course that may affect the performance of students in higher education institutions used in this study to examine students' academic achievement and predict students' performance to distinguish at-risk students are the marks of assignments, mid-term, lab exams, semester marks, total, grade, grade point (G.P.), quality point (Q.P.), grade point average (G.P.A.), and credit hours data of multiple courses categorized according to three knowledge areas defined by Higher Education Commission (H.E.C), Pakistan using data mining predictive techniques. The results indicate that the proposed methods can achieve maximum accuracy in predicting and identifying at-risk students in different courses.

Applying Predictive Data Mining to Discover Factors Associated to the Language Skill Performance from Elementary School Students

In this paper, predictive data mining techniques are applied to determine the academic performance from fifth grade students in the Saber 5° tests Language skill at Colombian elementary schools in 2017. We employed the CRISP-DM methodology. Socioeconomic, academic, and institutional information was available at the ICFES databases. A minable dataset was obtained using data cleaning and transformation techniques. A decision tree was built with the Weka tool J48 algorithm. Some of the predictors of the discovered patterns are the nature and location of the school, whether or not students failed a school year, the age group, the mother's educational attainment, and the rates of ICTs and household appliances. The findings of this research serve as quality information for the decision-making at the Ministry of National Education (MEN) and the secretaries of education, and for the directors of elementary educational institutions to define improvement plans that result in the quality of elementary school education in Colombia.

Data Mining Prediction Techniques in Health Care Sector

Knowledge discovery in databases (KDD) is another name of Data mining. It is an interdisciplinary area which focuses on extraction of useful knowledge from data in every sector like health, education, business etc. Nowadays, as covid pandemic is affecting everyone and due to surge in coronavirus cases causing shortage of hospital beds, oxygen supplies, vaccine and turning away patients from hospitals, put creaky health infrastructure in spotlight. The plenty of data is available in the medical field of these conditions. Data mining approaches can be used to extract useful patterns from these types of data to follow the upcoming trends. This paper focuses on various data mining techniques which can be used in medical industries for the best outcome.

A comparative analysis of data mining techniques for agricultural and hydrological drought prediction in the eastern Mediterranean

Drought is a natural hazard which affects ecosystems in the eastern Mediterranean. However, limited historical data for drought monitoring and forecasting are available in the eastern Mediterranean. Thus, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this context, the main goals of this research were to capture agricultural and hydrological drought trends by using the Standardized Precipitation Index (SPI) and to assess the applicability of four ML algorithms (bagging (BG), random subspace (RSS), random tree (RT), and random forest (RF)) in predicting drought events in the eastern Mediterranean based on SPI-3 and SPI-12. The results reveal that hydrological drought (SPI-12, −24) was more severe over the study area, where most stations showed a significant (p < 0.05) negative trend. The accuracy of ML algorithms in drought prediction varied in relation to the implementation stage. In the training stage, RT outperformed the other algorithms (Root mean square error (RMSE) = 0.3, Correlation Coefficient (r) = 0.97); the performance of the algorithms can be ranked as follows: RT > RF > BG > RSS for both SPI-3 and SPI-12. In the testing stage, both the BG and RF algorithms had the highest correlation r(observed vs. predicted) (0.58–0.64) and lowest RMSE (0.68–0.88). In contrast, the lowest correlation r(observed vs. predicted) (0.3–0.41) and highest RMSE (0.94–1.10) was calculated for the RT algorithm. However, BG was more dynamic in drought capturing, with the lowest RMSE and highest correlation. In the validation stage, the BG performance was satisfactory (RMSE = 0.62–0.83, r = 0.58–0.79). The output of this research will help decision-makers with drought mitigation plans by using the new four machine learning algorithms.

A Comprehensive Review Study on: Optimized Data Mining, Machine Learning and Deep Learning Techniques for Breast Cancer Prediction in Big Data Context

In recent years, big data in health care is commonly used for the prediction of diseases. The most common cancer is breast cancer infections of metropolitan Indian women as well as in women worldwide with a broadly factor occurrence among nations and regions. According to WHO, among 14% of all cancer tumours in women breast cancer is well-known cancer in women in India also. Few researches have been done on breast cancer prediction on Big data. Big data is now triggering a revolution in healthcare, resulting in better and more optimized outcomes. Rapid technological advancements have increased data generation; EHR (Electronic Health Record) systems produce a massive amount of patient-level data. In the healthcare industry, applications of big data will help to improve outcomes. However, the traditional prediction models have less efficiency in terms of accuracy and error rate. This review article is about the comparative assessment of complex data mining, machine learning, deep learning models used for identifying breast cancer because accuracy rate of any particular algorithm depends on various factors such as implementation framework, datasets(small or large),types of dataset used(attribute based or image based)etc. Aim of this review article is to help to choose the appropriate breast cancer prediction techniques specifically in the Big data environment to produce effective and efficient result, Because “Early detection is the key to prevention-in case of any cancer”.

Big data based analytic model to predict and classify breast cancer using improved fractional rough fuzzy K‐means clustering and labeled ensemble classifier algorithm

AbstractBreast cancer is a very dangerous disease that mainly affects women. It is a deadliest disease that highly affects the women's life. Therefore, it is necessary to predict and classify this deadly disease for early diagnosis. There exist numerous data mining techniques for early prediction and classification of this disease. The big data based analytical model provides the better solution for storing, manipulating, and analyzing a great number of mammographic images. In this article, a new improved fractional rough fuzzy K‐means clustering strategy is considered for disease prediction. Then, a new Tunicate Swarm Algorithm (TSA) is introduced to optimize the weight parameters. TSA is a bio‐inspired metaheuristic optimization approach. Finally, the labeled ensemble classifier (LEC) is utilized for classifying the stages of breast cancer as malignant and benign. Here, the data is randomly generated from breast cancer Wisconsin dataset (diagnosis) obtainable on UCI machine learning repository. The proposed strategy is compared with different existing strategies, like Logistic Regression Classifier, Random Forest Classifier. From the analysis, it is observed that the proposed big data based analytical model using LEC provides 99.3% accuracy that is very high when compared to the accuracy of existing approaches.

Optimal artificial neural network-based data mining technique for stress prediction in working employees

Depression has become a common issue among IT industry professionals today. Lifestyle changes and new work culture increase the risk of depression among employees. Various companies and organizations offer mental health plans and try to pacify the work environment. However, the problem is already out of control. This research paper proposes an effective deep learning model for stress prediction among working employees with the help of lion optimization-based optimal artificial neural network (OANN) model. Here, the features are selected using optimal ANN technique and the diseases are predicted using lion optimization method. ANN technique eliminates inappropriate and unnecessary attributes in a significant manner, once the information on calculated characteristics and weight is disseminated to lion optimization classifier. The test results inferred that the artificial neural network is highly efficient than the current OANN algorithm method, based on lion optimization. The study evaluated the data and found that the performance of employees working under normal conditions was higher when compared to the performance of employees who work under stress. Furthermore, attitude-coping efforts may be a cognitive behavioral mechanism, which explains how workload is related to courage and work performance of employees with high stress level.

Applying data mining techniques for technology prediction in new energy vehicle: a case study in China.

Technology prediction is an important technique to help new energy vehicle (NEV) firms keep market advantage and sustainable development. Under fierce competition in the new energy industry, there is an urgent necessity for innovative technology prediction method to effectively identify core and frontier technologies for NEV firms. Among the various methods of technology prediction, one of the most frequently used methods is to make technology prediction from patent data. This paper synthesizes the frequent pattern growth(FP-growth) algorithm and input-output analysis to construct a new technology prediction method based on the knowledge flow perspective, takes the data of NEV patent family in 1989-2018 the Derwent patent database as a sample, divides the data according to the 5-year standard, and uses the method to identify the core and frontier technologies in the NEV field during different periods. Furthermore, the multiple co-occurrence method applies to analyze the technology layout and evolution patterns in China's NEV field. The results show that the technology prediction method proposed in this paper can effectively identify core and frontier technologies to achieve NEV technology prediction.

Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural network

Software defect prediction aims to identify the potential defects of new software modules in advance by constructing an effective prediction model. However, the model performance is susceptible to irrelevant and redundant features. In addition, previous studies mainly use traditional data mining or machine learning techniques for defect prediction, the prediction performance is not superior enough. For the first issue, motivated by the idea of search based software engineering, we leverage the recently proposed whale optimization algorithm (WOA) and another complementary simulated annealing (SA) to construct an enhanced metaheuristic search based feature selection algorithm named EMWS, which can effectively select fewer but closely related representative features. For the second issue, we employ a hybrid deep neural network — convolutional neural network (CNN) and kernel extreme learning machine (KELM) to construct a unified defect prediction predictor called WSHCKE, which can further integrate the selected features into the abstract deep semantic features by CNN and boost the prediction performance by taking full advantage of the strong classification capacity of KELM. We conduct extensive experiments for feature selection or extraction and defect prediction across 20 widely-studied software projects on four evaluation indicators. Experimental results demonstrate the superiority of EMWS and WSHCKE.

İtalya’daki CO2 Emisyon Tahmini için Veri Madenciliği

This study is a preliminary evaluation of the situation of CO2 emissions in Italy, reviewing the international and national literature, using global datasets, and using data mining techniques for analysis and prediction. The study used descriptive methods. It focuses on finding the main potential parameters that effect the concentration of CO2 emissions based on energy resources in Italy. SMOreg, Linear Regression, and Simple Linear Regression are used. Based on the analysis, the Liquid Fuel sector has had the highest rate of increase in CO2 emission 56.8%. R. Linear Regression algorithm gives us a better performance of the prediction for the CO2 emissions than the second algorithm Simple Linear Regression. These results are in line with the present condition in Italy due to the Italian National Program on Climate Change which focuses on reducing carbon dioxide emissions.

A Comparative Analysis of Decision Trees Vis-`a-vis Other Computational Data Mining Techniques in Automotive Insurance Fraud Detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists.

Know Your Stars Before They Fall Apart: A Social Network Analysis of Telecom Industry to Foster Employee Retention Using Data Mining Technique

Social network analysis (SNA) has emerged as a significant paradigm for research in data mining community for measuring and analyzing human dynamic network structure. At organizational level, SNA can enhance our understanding of work place social interactions and unveil the hidden stars embedded in informal networks by investigating nodes and edges of complex networks. For this study, we aim to formulate a network centrality based quantitative method to identify the High potential employees (HiPos) and Influencers of telecom sector and explore the relationship between degree centrality of these star employees and their turnover intention by modeling the dynamics of their workplace social ties and predictive data mining technique. We investigated the multiplex work and advice network in two leading telecom operators of Pakistan i.e. Ufone and Zong. For the statistical analysis we conducted a quantitative and visual network analysis in UCINET along with correlation and regression. Our results showed a negative correlation between HiPos out-degree centrality and turnover intention and a positive correlation between influencer’s in-degree centrality and turnover. Whereas perceived investment in employee development (PIED) was found to mediate the relationship between in-degree centrality of influencer and turnover intention. The correlation results were then verified in regression model. These findings will guide the telecom operators in designing an optimal structure for business intelligence by providing critical insights of their star employees and help them to investigate the influence of central nodes on dynamical processes of its heterogeneous networks and thus enhance employee retention before a star falls out.

Educational Data Mining in Practice Literature Review

Educational Data Mining (EDM) is an evolving field with a suite of computational and psychological methods for understanding how students learn. Applying Data Mining methods to education data help us to resolve educational investigation issues. The growth of education data offers some unique advantages as well as some new challenges for education study. Some of the challenges are an improvement of student models, identify domain structure model, pedagogical support and extend educational theories. The main objective of this paper is to present the capabilities of data mining in the context of the higher educational system and their applications and progress, through a survey of literature and the classification of articles. We observed the works on investigational situation studies showed in the EDM during the recent past, in addition, we have introduced three data models based on descriptive and predictive data mining techniques. This is oriented towards students in order to recommend learners’ activities, resources, suggest path pruning and shortening or simply links that would favor and improve their learning or to educators in order to get more objective feedback for instruction.

Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage

Preterm birth, defined as a delivery before 37 weeks’ gestation, continues to affect 8–15% of all pregnancies and is associated with significant neonatal morbidity and mortality. Effective prediction of timing of delivery among women identified to be at significant risk for preterm birth would allow proper implementation of prophylactic therapeutic interventions. This paper aims first to develop a model that acts as a decision support system for pregnant women at high risk of delivering prematurely before having cervical cerclage. The model will predict whether the pregnancy will continue beyond 26 weeks’ gestation and the potential value of adding the cerclage in prolonging the pregnancy. The second aim is to develop a model that predicts the timing of spontaneous delivery in this high risk cohort after cerclage. The model will help treating physicians to define the chronology of management in relation to the risk of preterm birth, reducing the neonatal complications associated with it. Data from 274 pregnancies managed with cervical cerclage were included. 29 of the procedures involved multiple pregnancies. To build the first model, a data balancing technique called SMOTE was applied to overcome the problem of highly imbalanced class distribution in the dataset. After that, four classification models, namely Decision Tree, Random Forest, K-Nearest Neighbors (K-NN), and Neural Network (NN) were used to build the prediction model. The results showed that Random Forest classifier gave the best results in terms of G-mean and sensitivity with values of 0.96 and 1.00, respectively. These results were achieved at an oversampling ratio of 200%. For the second prediction model, five classification models were used to predict the time of spontaneous delivery; linear regression, Gaussian process, Random Forest, K-star, and LWL classifier. The Random Forest classifier performed best, with 0.752 correlation value. In conclusion, computational models can be developed to predict the need for cerclage and the gestation of delivery after this procedure. These models have moderate/high sensitivity for clinical application.