Data Mining Techniques Research Articles

Penilaian Pembayaran Kredit dengan Logistic Regression dan Random Forest pada Home Credit

Global economic development has led to the high complexity of society's needs. Financial institutions are here to provide facilities to meet the increasingly complex needs of society. However, the existence of problem loans can be a serious threat so classification techniques in data mining are used to overcome this problem. This research develops a model that can predict customers' ability to make credit payments so that financial institutions can avoid problematic credit. In this research, the SMOTE resampling technique is used to see the effect of sampling in dealing with class imbalance and conducting credit assessments. The research results show that the model built using SMOTE has better AUC than the model without SMOTE. From the two machine learning algorithms, logistic regression and random forest, the results show that the random forest model with SMOTE has the best performance with an accuracy value of 90%, precision of 92%, recall of 88%, F1-score of 90%, and AUC value of 0.97. Based on the best model, ten important features were obtained that influence the process of assessing credit repayment capabilities, namely the normalized score from external data sources, the period for changing customer numbers, the number of previous installment payments, the customer's age, registration time, the period for applying for credit at the credit bureau, the period for changing identity documents, the time for updating information at the credit bureau, and the length of time the customer has worked. In addition, this research produces visualizations via dashboards that can be used to improve the process of assessing credit repayment capabilities.

Menyusun Display Barang dengan Data Mining Assosiation Rule Menggunakan Rapid Miner Algoritma FP-GROWTH

A cosmetic cheerful shop located in the Marelan area of Medan city is a shop that sells women's cosmetic equipment. The owner of a cheerful cosmetics shop is very disciplined in recording sales and purchases of goods. Every transaction is always recorded and stored in the Microsoft Excel application. Meanwhile, the Ceria Cosmetics store has been running since 2015 and has recorded lots of transactions, both sales and purchases. From 2015 until now the data has been increasing, and so far this data has only been a record and stored in a folder on the store's computer. From the piles of transaction data at the cheerful cosmetics store, the authors took the idea of research to utilize this pile of data. One of the applications of information technology that can be used to utilize the pile of data is the data mining technique. By using data mining, a pile of data from a cheerful cosmetic store can be used to find association patterns. And the application of data mining with association rules can use the FP-Growth algorithm. The FP-Growth algorithm can be used as an alternative to determine the most frequently occurring data set (frequent itemset) in a data set. Later, the combination patterns obtained in this study can be used to arrange the layout of goods (cosmetics) at the Ceria Cosmetics store in Medan Marelan.

Enhancing Book Recommendation Accuracy through User Rating Analysis and Collaborative Filtering Techniques

Since online electronic books have become popular, book recommendation systems have been invented and challenged to handle the high demand from users in the digital era. This study aimed to develop and evaluate a book recommendation model using data mining techniques through RapidMiner Studio. The datasets used were comprised of 981,756 user ratings. Before conducting the data analytics, the data was pre-processed to eliminate duplicates and retain only the highest ratings. Collaborative Filtering (CF) techniques, particularly k-Nearest Neighbours (k-NN) and Matrix Factorization (KF), were employed to elicit insightful information for development and to highlight their capabilities in handling enormous datasets. Furthermore, statistical analysis, visualization, elementary modelling, and model combinations were investigated to compare their performance. To reinforce creditability, modelling techniques and parameter adjustments were integrated to optimize the performance of the algorithms, since the results indicated that different model settings and data partitions impacted the effectiveness of the recommendation system. Additionally, these results demonstrated the potential of hybrid models in improving the accuracy and efficiency of recommendation systems and highlighted the trade-off between algorithmic approaches and dataset characteristics that interplay in optimizing the performance of recommendation systems.

A Judicialização da Saúde na era do Big Data

The judicialization of health in Brazil, through legal actions to guarantee access to medicines and health services, complicates health policies and serves different interests. Accurately accounting for these actions is challenging, highlighting divergences between data sources. Analysis of data from Minas Gerais reveals significant numbers, but highlights challenges in classifying and standardizing information. Research showed that, with data mining techniques and standardization of the definition of health processes, it is possible to move in this direction. Tools like JUDJe promise to improve understanding of the judicialization of health, facing technological, privacy and security challenges. It is concluded that there is a need to widely incorporate these tools and standardize matters, to benefit the management of the health sector throughout Brazil. Submission: 04/24/24| Review: 05/15/24| Approval: 05/18/24

Implementation of Random Forest with Synthetic Minority Oversampling Technique and Particle Swarm Optimization for Predicting Survival of Heart Failure Patients

Abstract. Heart failure is caused by a disruption in the heart’s muscle wall, which results in the heart’s inability to pump blood in sufficient quantities to meet the body’s demand for blood. The increasing prevalence and mortality rates of heart failure can be reduced through early disease detection using data mining processes. Data mining is believed to aid in discovering and interpreting specific patterns in decision-making based on processed information. Data mining has also been applied in various fields, one of which is the healthcare sector. One of the data mining techniques used to predict a decision is the classification technique. Purpose: This research aims to apply SMOTE and PSO to the Random Forest classification algorithm in predicting the survival of heart failure patients and to determine its accuracy results. Methods/Study design/approach: To predict the survival of heart failure patients, we utilize the Random Forest classification algorithm and incorporate data imbalance handling with SMOTE and feature selection techniques with PSO on the Heart Failure Clinical Records Dataset. The data mining process consists of three distinct phases. Result/Findings: The application of SMOTE and PSO on the Heart Failure Clinical Records Dataset in the Random Forest classification process resulted in an accuracy rate of 93.9%. In contrast, the Random Forest classification process without SMOTE and PSO resulted in an accuracy rate of only 88.33%. This indicates that the proposed method combination can optimize the performance of the classification algorithm, achieving a higher accuracy compared to previous research. Novelty/Originality/Value: Data imbalance and irrelevant features in the Heart Failure Clinical Records Dataset significantly impact the classification process. Therefore, this research utilizes SMOTE as a data balancing method and PSO as a feature selection technique in the Heart Failure Clinical Records Dataset before the classification process of the Random Forest algorithm.

The impact of predictive analytics on financial risk management in businesses

This paper examines the impact of predictive analytics on financial risk management in businesses. Predictive analytics involves the use of statistical algorithms, machine learning, and data mining techniques to analyze historical data and predict future outcomes. In the context of financial risk management, predictive analytics plays a critical role in identifying, assessing, and mitigating potential financial risks. This paper explores various machine learning algorithms, including neural networks, decision trees, and support vector machines, and their applications in risk management. It also discusses data sources, preprocessing techniques, and the challenges associated with data privacy, model interpretability, and prediction accuracy. The review highlights successful implementations of predictive analytics in financial risk management and provides recommendations for future research and practical applications. As predictive analytics continues to advance, its integration with emerging technologies such as artificial intelligence and blockchain promises to enhance financial risk management practices.

Optimizing Random Forest for Predicting Thoracic Surgery Success in Lung Cancer Using Recursive Feature Elimination and GridSearchCV

Abstract. Lung cancer is one of the deadliest forms of cancer, claiming numerous lives annually. Thoracic surgery is a strategy to manage lung cancer patients; however, it poses high risks, including potential nerve damage and fatal complications leading to mortality. Predicting the success rate of thoracic surgery for lung cancer patients can be accomplished using data mining techniques based on classification principles. Medical data mining involves employing mathematical, statistical, and computational methods. In this study, the prediction of thoracic surgery success employs the random forest algorithm with recursive feature elimination for feature selection. The feature selection process yields the top 8 features. The 8 best features include 'DGN', 'PRE4', 'PRE5', 'PRE6', 'PRE10', 'PRE14', 'PRE30', and 'AGE'. Hyperparameter using GridSearchCV is then applied to enhance classification accuracy. The results of this method implementation demonstrate a predictive accuracy of 91.41%. Purpose: The study aims to develop and evaluate a Random Forest model with a Recursive Feature Elimination feature selection and applies hyperparameter GridSearchCV for predicting thoracic surgery success rate. Methods: This study uses the thoracic surgery dataset and applies various data preprocessing techniques. The dataset is then used for classification using the Random Forest algorithm and applies the Recursive Feature Elimination feature selection to obtain the best features. GridSearchCV is used in this study for hyperparameter. Result: The accuracy using the Random Forest algorithm and Recursive Feature Elimination feature selection with hyperparameters tuning GridSearchCV resulted in an accuracy of 91,41%. The accuracy was obtained from the following parameters values: bootstrap set to false, criterion set to gini, n_estimator equal to 100, max_depth set to none, min_samples_split equal to 4, min_samples_leaf equal to 1, max_features set to auto, n_jobs set to -1, and verbose set to 2 with 10-fold cross validation. Novelty: This study comparison and analysis of various dataset preprocessing methods and different model configurations are conducted to find the best model for predicting the success rate of thoracic surgery. The study also employs feature selection to choose the best feature to be used in classification process, as well as hyperparameter tuning to achieve optimal accuracy and discover the optimal values for these hyperparameters.

Food for Thought in Malaysian Borneo: Data Mining and Sentiment Analysis of Tripadvisor Reviews for Online Destination Branding

Unique destination branding in tourism is crucial to gaining a competitive advantage. Local food is a significant component of a destination brand which Destination Management Organisations (DMOs) must integrate into their official marketing efforts. DMOs must monitor the popularity and perception of their food promotions against the competition. Online promotion and management are challenging due to the rise of user-generated content. Automatic data mining techniques were used to determine the extent to which tourist food choices on TripAdvisor matched foods promoted by DMOs and how distinct these choices were from a rival DMOs’ promotion. We compared online food promotion between the Sabah Tourism Board and the Sarawak Tourism Board. We developed a software system to automatically extract food branding business intelligence from TripAdvisor restaurant reviews. The application of web crawling and scraping technology was applied to extract data and use Sentiment Analysis for interpretation. Online foods promoted by DMOs for each region were found to contain only a few common dishes, but these were more popular than region-specific foods. Significantly different distributions of food choices were found for each region. Some potentially useful differences between foreign and domestic tourists and locals were also identified. Sentiment analysis revealed hidden information in reviews useful for potential food destination branding. Findings from TripAdvisor confirm practical suggestions for improving brand distinctiveness found in the literature. This study is the first to develop an actual system that DMOs could use to estimate the online popularity of their promoted foods and those of their competition.

An automated approach to predict diabetic patients using KNN imputation and effective data mining techniques

Diabetes is thought to be the most common illness in underdeveloped nations. Early detection and competent medical care are crucial steps in reducing the effects of diabetes. Examining the signs associated with diabetes is one of the most effective ways to identify the condition. The problem of missing data is not very well investigated in existing works. In addition, existing studies on diabetes detection lack accuracy and robustness. The available datasets frequently contain missing information for the automated detection of diabetes, which might negatively impact machine learning model performance. This work suggests an automated diabetes prediction method that achieves high accuracy and effectively manages missing variables in order to address this problem. The proposed strategy employs a stacked ensemble voting classifier model with three machine learning models. and a KNN Imputer to handle missing values. Using the KNN imputer, the suggested model performs exceptionally well, with accuracy, precision, recall, F1 score, and MCC of 98.59%, 99.26%, 99.75%, 99.45%, and 99.24%, respectively. In two scenarios one with missing values eliminated and the other with KNN imputer, the study thoroughly compared the suggested model with seven other machine learning techniques. The outcomes demonstrate the superiority of the suggested model over current state-of-the-art methods and confirm its efficacy. This work demonstrates the capability of KNN imputer and looks at the problem of missing values for diabetes detection. Medical professionals can utilize the results to improve care for diabetes patients and discover problems early.

Discriminative fuzzy [formula omitted]-means clustering with local structure preservation for high-dimensional data

K-means clustering, which aims to distribute data points into K different clusters, is an effective technique for data mining and machine learning. However, several factors degrade clustering performance in real-world applications. High-dimensional data containing redundant features is one of the key issues in the clustering process. Many traditional hard or fuzzy versions of K-means clustering fail to handle high-dimensional data effectively. In this paper, we present a novel model referred to as discriminative fuzzy K-means clustering, which incorporates discriminative projection and p-Laplacian graph regularization into a joint framework. Specifically, the proposed model can reduce the negative impact of redundant features and enhance the discriminative power when projecting the original data into a lower-dimensional space. It simultaneously preserves the structural locality properties of the data by performing p-Laplacian graph regularization on the membership matrix. Consequently, clustering performance is significantly enhanced for high-dimensional data. Finally, an effective algorithm is presented to solve the formulated optimization problem. Experimental results obtained on benchmark datasets are promising and demonstrate the superiority of the proposed method.

Safety assessment of KRAS (G12C) inhibitors based on the FDA Adverse Event Reporting System (FAERS) database: A real-world pharmacovigilance study

ObjectivesKRAS (G12C) inhibitors (sotorasib and adagrasib) have approved treatment in patients with KRAS (G12C)-mutated non-small cell lung cancer (NSCLC). The post-marketing data concerning KRAS (G12C) inhibitors remain limited, and the outcomes of relevant studies are yet to yield conclusive evidence supporting the long-term safety of KRAS (G12C) inhibitors. Materials and methodsThis investigation comprehensively assessed adverse events (AEs) attributed to KRAS (G12C) inhibitors by employing advanced data mining techniques, utilizing the FDA Adverse Event Reporting System (FAERS). The dataset encompasses the period from the first quarter of 2021 to the first quarter of 2024. A disproportionality analysis was conducted to quantify the correlation between KRAS (G12C) inhibitors and AEs. The metrics employed for the evaluation of disproportionality comprise the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the information component (IC), and the empirical Bayesian geometric mean (EBGM). ResultsA total of 2,253 and 486 reports were identified as related to sotorasib and adagrasib, with the identification of 51 and 26 preferred terms, respectively. The most frequent AEs of sotorasib comprised diarrhoea (ROR 5.27), hepatotoxicity (ROR 38.09), alanine aminotransferase increased (ROR 17.41), aspartate aminotransferase increased (ROR 20.88), and hepatic function abnormal (ROR 19.88). The most common AEs of adagrasib included diarrhoea (ROR 4.21), nausea (ROR 3.84), vomiting (ROR 5.36), decreased appetite (ROR 4.79), and dehydration (ROR 7.00). A relatively reduced risk of hepatotoxicity but a increased risk of serious AEs in adagrasib compared to sotorasib (P < 0.001). ConclusionOur findings would provide valued evidence for healthcare professionals to recognize AEs associated with KRAS (G12C) inhibitors and differences between sotorasib and adagrasib, and guide their clinical practice.

Analysis of Kinematics Graph Interpretation Skills Using RapidMiner

This study explores the application of data mining techniques in physics education research, focusing on the analysis of kinematics graph interpretation skills. The research had two main objectives: to demonstrate the utility of RapidMiner, a data mining tool, in analyzing educational data, and to compare its effectiveness with traditional item analysis methods. Fifty-nine Grade-10 students at Chiang Mai University Demonstration School completed the Test of Understanding Graphs in Kinematics (TUG-K) before and after participating in a problem-based learning module integrating high-speed video analysis. Traditional statistical analysis revealed significant improvement in student performance (p&lt;0.001, effect size 0.76). Association rule mining, conducted using RapidMiner, uncovered key relationships between test items that were not apparent through traditional analysis. These relationships provided insights into common student misconceptions and areas requiring targeted instruction. The study demonstrates the potential of advanced data mining techniques to reveal deeper patterns in educational data compared to conventional item analysis methods. This novel application of RapidMiner in physics education research offers a promising approach for more detailed analysis of student understanding, potentially informing more effective teaching strategies and curriculum design in physics education.

DATA ANALYSIS AND MACHINE LEARNING APPLICATIONS IN ENVIRONMENTAL MANAGEMENT

The rapid expansion of data on air contaminants and climate change, particularly concerning public health, presents both opportunities and challenges for traditional epidemiological methods. This study aims to address these challenges by exploring advanced data collection, pattern identification, and predictive modeling techniques in the context of air pollution research. The focus is leveraging data mining and computational methods to enhance the understanding of air pollution's impact on public health, specifically ozone exposure. A comprehensive review of the scientific literature was conducted, utilizing databases such as Professor, Scholar, Embl, and Nih to identify relevant studies on air pollution epidemiology. The review highlights the integration of data mining, machine learning, and spatiotemporal modeling to improve the detection, analysis, and forecasting of air pollution-related health issues. The findings reveal a growing trend in applying data mining techniques within the field of air pollution epidemiology. Advanced methods, such as spatiotemporal analysis and geographic data mining, enable more precise tracking and forecasting of pollution-related health risks. Continuous advancements in artificial intelligence and the development of more sophisticated sensors and data storage technologies are enhancing the accuracy and reliability of air quality monitoring and public health predictions. This study highlights the transformative potential of integrating data mining and AI techniques into air pollution epidemiology. Exploring emerging technologies like spatiotemporal mining and next-generation sensors paves the way for more accurate, timely, and scalable solutions to monitor air quality and predict its impact on public health, opening new avenues for research and policy interventions.

Development of Ergo-Bundling System for Food Retailer based on Data Mining

In retail, a major challenge is quickly selling perishable food items like cakes or pastries within a limited time. If unsold, these products lead to losses, even with discount promotions. Retailers must forecast stock accurately and develop more effective strategies to meet consumer demand within the required timeframe. One suggested strategy is bundling, where high-demand products are combined with less popular ones. However, when done manually, this approach often fails to align with consumer preferences. This study aims to develop an automated bundling system using data mining techniques. Market Basket Analysis is used to understand consumer purchasing patterns, while Association Rules with the Apriori Algorithm help identify relationships between different products. These methods reveal which items are frequently bought together, making bundling strategies more effective. The system will be designed with usability and ergonomic principles, ensuring it is user-friendly. The implications of this system include improved stock management, more accurate bundling, and better alignment with customer preferences, ultimately increasing satisfaction. Additionally, automation reduces errors and inconsistencies that occur with manual bundling. The expected outcome is a more efficient, effective, and comfortable system for food retailers, leading to higher sales, reduced losses, and greater overall customer satisfaction.

Data Management Analysis for Predicting Stroke using RapidMiner

Stroke are known as the second most leading cause of death. Because of this, data mining techniques are already being used to predict patients that may have stroke. Therefore, we are doing a study to try using data mining techniques using RapidMiner to find information or patterns regarding stroke from a dataset obtained from Kaggle. Three data mining techniques are used in this study, that is classification using decision trees, association rule using FP-Growth algorithm, and clustering technique using k-Means algorithm. Using RapidMiner, we are able to process the dataset using the operators provided in the application. As the result, we found out that due to an unbalanced data, the decision tree model made were only able to predict 68,75% patients as having stroke. With the association rule technique, we found out that most attributes in the dataset does not really associated with each other. With the clustering technique, we were able to group up patients and found out that most patients that have stroke are averaged in the age of 58, with 31 bmi, and 201 average glucose level.

Sustainability of Quality Management by Implementing Data Mining to Predict Academic Achievement

This research aims to predict the academic achievement of Pamulang University students using Educational Data Mining (EDM) techniques. With the increasing number of students and the complexity of academic data, it is important to apply methods that can analyze and predict learning outcomes to improve learning strategies and academic support. This study collected data from various sources, including course grades, attendance, and participation in extracurricular activities. The collected data is then analyzed using EDM techniques such as decision trees, neural networks, and support vector machines to identify patterns and factors that contribute to student academic achievement. The results of the analysis show that factors such as attendance, involvement in campus activities, and previous test scores have a significant influence on academic achievement. This research provides valuable insights for the development of targeted interventions, with the aim of improving academic outcomes and facilitating more effective learning strategies at Pamulang University. These findings also offer contributions to further research in the field of EDM and its application in higher education contexts.

Klasterisasi Penggunaan Ban dengan Cost Per Kilometer Terendah pada PT. PL menggunakan Metode K-Means

Established in 1969, PT. Puninar has grown to become one of Indonesia's leading logistics companies. The company provides a broad range of logistics solutions through its subsidiaries, encompassing material management, manufacturing, warehousing, distribution, customs services, freight forwarding, and post-delivery operations. After fuel, tires represent the second-largest expenditure for the company. Proper tire management can reduce maintenance costs, making it a key cost-reduction strategy in the logistics sector. This study utilizes data mining techniques, specifically the K-means method, to analyze and classify tires based on travel distance and the lowest cost per kilometer, enabling monthly cost monitoring. PT. Puninar allocates approximately two billion rupiah per month for tire inventory due to its fleet of over seven hundred trucks. The company currently employs various tire brands, including Bridgestone, Doublecoin, Effiplus, Chengsang, and others, with varying levels of durability.

Structure and Density Heterogeneities of Silica Glass: Insight from Datamining Techniques

Structure and Density Heterogeneities of Silica Glass: Insight from Datamining Techniques

Comparison of RNN-LSTM, TFDF and stacking model approach for weather forecasting in Bangladesh using historical data from 1963 to 2022

Forecasting the weather in an area characterized by erratic weather patterns and unpredictable climate change is a challenging endeavour. The weather is classified as a non-linear system since it is influenced by various factors that contribute to climate change, such as humidity, average temperature, sea level pressure, and rainfall. A reliable forecasting system is crucial in several industries, including transportation, agriculture, tourism, &amp; development. This study showcases the effectiveness of data mining, meteorological analysis, and machine learning techniques such as RNN-LSTM, TensorFlow Decision Forest (TFDF), and model stacking (including ElasticNet, GradientBoost, KRR, and Lasso) in improving the precision and dependability of weather forecasting. The stacking model strategy entails aggregating multiple base models into a meta-model to address issues of overfitting and underfitting, hence improving the accuracy of the prediction model. To carry out the study, a comprehensive 60-year meteorological record from Bangladesh was gathered, encompassing data on rainfall, humidity, average temperature, and sea level pressure. The results of this study suggest that the stacking average model outperforms the TFDF and RNN-LSTM models in predicting average temperature. The stacking average model achieves an RMSLE of 1.3002, which is a 10.906% improvement compared to the TFDF model. It is worth noting that the TFDF model had previously outperformed the RNN-LSTM model. The performance of the individual stacking model is not as impressive as that of the average model, with the validation results being better in TFDF.

Parametric optimization and comparative study of machine learning and deep learning algorithms for breast cancer diagnosis.

Breast Cancer is the leading form of cancer found in women and a major cause of increased mortality rates among them. However, manual diagnosis of the disease is time-consuming and often limited by the availability of screening systems. Thus, there is a pressing need for an automatic diagnosis system that can quickly detect cancer in its early stages. Data mining and machine learning techniques have emerged as valuable tools in developing such a system. In this study we investigated the performance of several machine learning models on the Wisconsin Breast Cancer (original) dataset with a particular emphasis on finding which models perform the best for breast cancer diagnosis. The study also explores the contrast between the proposed ANN methodology and conventional machine learning techniques. The comparison between the methods employed in the current study and those utilized in earlier research on the Wisconsin Breast Cancer dataset is also compared. The findings of this study are in line with those of previous studies which also highlighted the efficacy of SVM, Decision Tree, CART, ANN, and ELM ANN for breast cancer detection. Several classifiers achieved high accuracy, precision and F1 scores for benign and malignant tumours, respectively. It is also found that models with hyperparameter adjustment performed better than those without and boosting methods like as XGBoost, Adaboost, and Gradient Boost consistently performed well across benign and malignant tumours. The study emphasizes the significance of hyperparameter tuning and the efficacy of boosting algorithms in addressing the complexity and nonlinearity of data. Using the Wisconsin Breast Cancer (original) dataset, a detailed summary of the current status of research on breast cancer diagnosis is provided.