Data Mining Tools Research Articles

Uncovering Patterns: Data Mining the Deforestation Frontier in Brazilian Cerrado

Abstract. The Brazilian Cerrado, recognized as the world's most diverse tropical savanna, has undergone significant land clearance for agriculture, cattle ranching, and charcoal production, leading to considerable loss of its natural vegetation. This deforestation disrupts major aquifers, affecting ecosystems and human populations. The Cerrado Deforestation Monitoring Project (PRODES), active since 2017, systematically maps new deforestation using satellite imagery. This study used GeoDMA, a geospatial data mining tool for analyzing complex spatial databases such as those provided by the PRODES. The study area is the Alto Parnaíba region within the MATOPIBA agricultural frontier, characterized by high rates of crop expansion and burned areas. Using PRODES deforestation data from 2000 to 2023, the study categorizes deforestation patterns into geometric, multidirectional, and diffuse types, associated with large-scale agriculture, smallholder farming, and subsistence agriculture, respectively. The analysis reveals that approximately 50% of land use trajectories are small-scale subsistence agriculture, predominantly in northern Alto Parnaíba. Unchanged large-scale farms are concentrated in the southern part of the study area, linked to annual crops. The transition from small to large-scale farms is significant in the western region. Protected areas, especially strictly protected conservation units, show minimal changes in native vegetation. This study highlights the expansion of large-scale agriculture in MATOPIBA, emphasizing the need for targeted interventions to manage land use changes and prevent further deforestation. Future research should extend this approach to other Cerrado ecoregions to improve the detection and analysis of deforestation processes.

GCM and gcType in 2024: comprehensive resources for microbial strains and genomic data.

Microbial culture collections play a crucial role in the collection, maintenance, and distribution of quality-assured living microbial strains, along with their associated phenotypic and omics data. To enhance the find-able, accessible, interoperable, and re-usable (FAIR) data utilization of microbial resources, the World Data Center for Microorganisms (WDCM) has developed the Global Catalogue of Microorganisms (GCM) and the Global Catalogue of Type Strains (gcType). These platforms provide interactive interfaces for cataloging the holdings of collections, along with detailed annotations of type strain genomes and curated metadata, including ecosystems, growth conditions, and collection locations. The system maximizes the scientific impact of microbial resources and culture collections through an integrated data mining tool that links strain- and species-related information from various public resources. Currently, the GCM and gcType include 574422 strains from 154 culture collections across 51 countries and regions, along with 25980 genomes from type species. Additionally, 2702655 articles and 103337 patents are integrated with these microbial resources.The system supports microbial taxonomic research and provides evidence for implementing the Nagoya Protocol in the field of microbial resources and their digital sequence information (DSI). Access is freely available at gcm.wdcm.org and gctype.wdcm.org.

Exploring Core Knowledge in Interdisciplinary Research: Insights from Topic Modeling Analysis

Although interdisciplinary research has garnered extensive attention in academia, its core knowledge structure has yet to be systematically explored. To address this gap, this study aims to uncover the underlying core knowledge topics within interdisciplinary research, enabling researchers to gain a deeper understanding of the knowledge framework, improve research efficiency, and offer insights for future inquiries. Based on the Web of Science (WoS) database, this study collected 153 highly cited papers and employed the LDA topic model to identify latent topics and extract the knowledge structure within interdisciplinary research. The findings indicate that the core knowledge topics of interdisciplinary research can be categorized into four major areas: the knowledge framework and social impact of interdisciplinary research, multidisciplinary approaches in cancer treatment and patient care, Covid-19 multidisciplinary care and rehabilitation, and multidisciplinary AI and optimization in industrial applications. Moreover, the study reveals that AI-related interdisciplinary research topics are rapidly emerging. Through an in-depth analysis of these topics, the study discusses potential future directions for interdisciplinary research, including the cultivation and development of interdisciplinary talent, evaluation systems and policy support for interdisciplinary research, international cooperation and interdisciplinary globalization, and AI and interdisciplinary research optimization. This study not only uncovers the core knowledge structure of interdisciplinary research but also demonstrates the effectiveness of the LDA topic model as a data mining tool for revealing key topics and trends, providing practical tools for future research. However, this study has two main limitations: the time lag of highly cited papers and the dynamic evolution of interdisciplinary research. Future research should address these limitations to further enhance the understanding of interdisciplinary research.

Enhancing the accuracy of blood-glucose tests by upgrading FTIR with multiple-reflections, quantum cascade laser, two-dimensional correlation spectroscopy and machine learning

The accuracy of screening diabetes from non-diabetes is drastically enhanced by strategically upgrading the bench-marking infrared spectroscopy technique for non-invasive tests of blood-glucose, both with state-of-the-art instrumentation-retrofits and with intelligent spectral-datamining tools. First, the signal-to-noise performance of FTIR in measuring the spectral features of a glucose solution containing bovine serum albumin is improved by 2–3 times with the common single-pass attenuated total-reflection setup replaced by a multi-passes-reflections setup. Second, replacing the ordinary infrared lamp with a quantum cascade laser further improves the signal-to-noise by 3 times. The performance of the upgraded spectrometer in screening hyperglycemia is gauged by the accuracy of such screens derived from 100 repetitive spectral-measurements per glucose concentration, for 24 glucose concentrations spanning the range of 70–300 mg/dL, a range which covers the blood-glucose contents of all non-diabetic and diabetic human-subjects. Third, intelligent datamining methods are exploited to implement decision trees for screening hyperglycemia. Their decisions are mapped into a confusion matrix and the matrix-elements are used to calculate the accuracy merits of each method. Evidently, the accuracy of the multi-passes-FTIR with the standard principal-components datamining method is 80 %. The adoptions of the quantum cascade laser and two-dimensional correlation spectroscopy datamining technique raises this to 96.3 %. Finally, a novel machine learning method, which comprises three different decision-tree tools to generate trial screening decisions and a “majority-voting” datamining tool to reach a final screening decision, yields the best accuracy of 98.8 % ever reported in the literature.

Kiswahili Translation in Digital Media Platforms: Bridging Language Gaps in the AI Age

Purpose: As digital media platforms continue to proliferate globally, the need for multilingual content in these platforms has become increasingly apparent. Translation of online content is a necessity for netizens and other digital platform users who seek knowledge and information around a myriad of user-generated content on diverse issues and topics. Being a widely spoken language that goes beyond the borders of East Africa, Kiswahili’s significance in bridging linguistic barriers and facilitating communication and information access for Kiswahili speakers is clear. The purpose of this exploratory research paper was to examine the state of Kiswahili as a language of translation through different technologies across varied translation apps and social media platforms such as Facebook, You Tube, Tik Tok and Netflix. Methodology: The study adopted a descriptive research design. Data collection was done through online data mining tools including API access for web scrapping and manual extraction of content through random search engine searches. The data for the study included 10 sentences, 10 videos and 25 specific word forms such as proverbs, spiritual/religious words, similes and cultural words for food, beauty and family ties. Content analysis for both text and multimedia content such as written sentences, social media posts and videos was done. The study analyzed translation outputs of 14 Kiswahili translation apps in order to establish the status and accuracy of these translations. Of key consideration was the role of AI technology in the translation apps and the effect it has on the translated texts across different apps. The challenges and emerging issues facing Kiswahili translation through digital technologies were also explored. Findings: The findings of this study showed that the Apps used for Kiswahili translation have demonstrated great translation accuracy in basic semantic forms, with an average score of 60% accuracy upon random searches and between 10-30% accuracy for cultural forms and idiomatic expressions. Unique Contribution to Theory, Practice and Policy: The study was guided by the Skopos theory by Hans Vermeer (1978) which was complemented by the Relevance Theory by Dan Sperber and Deidre Wilson (1986). These Apps seem to have the feature of interoperability in that they seem to produce the same results suggesting that the AI supported feature in the apps may be interconnecting the apps during translation. The study advocates for more AI integration translation apps as this will help standardize Kiswahili translation corpus.

Predictive Regression Modeling for Forecasting Graduation Duration in Online Offsite Degree Program

The demand for Information Technology (IT) professionals continues to rise across various sectors, where theyplay vital roles. However, the supply of IT graduates often fails to meet industry needs and this is a huge problem for the SriLankan IT Industry (National IT-BPM Workforce Survey – 2019). In this context, this study presents a predictive regressionmodelling approach to predict graduation duration in the Bachelor of Information Technology (BIT) degree program at theUniversity of Moratuwa, Sri Lanka. It integrates demographic data—student district, birth year, AL results, OL maths grade,gender, employability status, occupation, and AL stream—along with academic performance indicators like diplomacompletions and higher diploma completions. After evaluating the suggested features, the key findings indicate thesignificance of certain features, notably the number of semesters taken to complete the diploma, higher diploma, and thedegree. Additionally, demographic factors such as district, birth year, AL results, OL maths grade, gender, and employabilitystatus were found to be important. The regression analysis was carried out using the Orange data mining tool (Orange DataMining). Various algorithms, including random forest, neural network, linear regression, and k-nearest neighbours (kNN),were used to develop predictive models. By adjusting parameters such as metrics, weights, number of neighbours, numberof iterations, and training dataset size, the models were optimised to better fit the dataset. Training and testing the modelsrevealed consistent error metrics, including MSE, RMSE, MAE, and R^2, validating the accuracy of predictions. Byconsidering the least and reasonable error in each model, the most suitable model to fit the given dataset was selected.The prediction model accurately forecasted graduation duration for subsequent academic batches, demonstrating itseffectiveness in predicting student progress in the program. This research contributes to understanding the factorsinfluencing graduation duration in a distance learning context and provides insights for educational institutions to optimiseprogram planning and student support initiatives. Additionally, it is a good indicator to the companies to gain a betterunderstanding of the availability of future workforce.

A Clustering Ensemble Method for Drug Safety Signal Detection in Post-Marketing Surveillance.

Post-marketing surveillance refers to the process of monitoring the safety of drugs once they reach the market, after the successful completion of clinical trials. In this work, we investigate a computational approach using data mining tools to detect safety signals from post-market safety databases, or in other words, to identify adverse events (AEs) with disproportionately high reporting rates compared to other AEs, associated with a particular drug or a drug class. Essentially, we view this as a problem of cluster analysis-based anomaly detection on post-market safety data, where the goal is to 'unsupervisedly' detect the anomalous or the signal AEs. Our findings demonstrate the potential of using a clustering ensemble method to detect drug safety signals. It employs multiple clustering or anomaly detection algorithms, followed by a performance comparison of the candidate algorithms based on a collection of appropriate measures of goodness of clustering results. The method is general enough to include any number of clustering or anomaly detection algorithms and goodness measures, and performs better than any of the candidate algorithms in identifying the signal AEs. Extensive simulation studies illustrate that the ensemble method detects the AE signals from synthetic post-market safety datasets pretty accurately across the different scenarios explored. Based on the cases reported to the FDA Adverse Event Reporting System (FAERS) between 2013 and 2022, we further demonstrate that the ensemble method successfully identifies and confirms most of the adverse events that are known to occur most frequently in reaction to antiepileptic drugs and -lactam antibiotics.

Improving the Imbalanced Data Accuracy Using CNN and ReLU

In today’s academic world, learning from datasets is a trendy topic. In the event of imbalanced data, none of the many data mining tools available for this purpose work well, partly because this type of data generates a range of minority classes, which might obstruct the learning process. In addition to its enormous volume, big data has the traits of speed and variety. In this paper, the authors have proposed a CNN model with a rectified linear activation function (ReLU) activation function to get good accuracy on an imbalanced dataset. The dataset used in this paper was electrocardiogram (ECG) heartbeat categorization.

WEKA: A Tool for Shaping Effective Student Learning Strategies for College School Leavers

As college school leavers transition into higher education or the workforce, they face numerous challenges in adapting to new learning environments and expectations. Effective learning strategies are crucial to ensuring their success in these new settings. This paper explores how WEKA, a powerful data mining tool, can be leveraged to shape and enhance student learning strategies for college school leavers. By analysing educational data, WEKA can provide insights into the most effective learning methods, helping educators tailor their approaches to meet the specific needs of these students. The paper discusses the application of WEKA in educational data mining, highlights its capabilities, and presents case studies demonstrating its effectiveness in shaping learning strategies.

Al-Balāghah’s Legacy in Modern Secular Journalism

Abstract Contemporary studies have already explored al-Risālah magazine’s (1933–1953) dynamic role in championing a far-reaching secular-grounded educational mission— which was made more urgent by the encounter with Western innovations. Still, little attention has been paid to the journal’s strong linkages with Arabic classical heritage. This article argues that al-Risālah’s writings employed concepts and terminology related to al-balāghah, or eloquence, that were by turns accurate, controversial, and innovative. These uses enriched the spectrum of al-balāghah’s applicability also to the discussion of current preoccupations with other fields like psychology. While accounting for thought-provoking works like Tāhā Ḥusayn’s al-Bayān al-ʿarabī min al-Jāḥiẓ ilā ʿAbd al-Qāhir [The Arabic Bayān, from al-Jāḥiẓ to ʿAbd al-Qāhir, 1933,] Amīn al-Khūlī’s al-Balāghah wa-ʿilm al-nafs [al-Balāghah and Psychology, 1939,] and al-Zayyāt’s Difāʿun ʿan al-balāghah [In Defense of al-Balāghah, 1945] as pioneering studies towards the new conceptualizations of the discipline up to the mid-half of the twentieth-century, this study purports to trace the presence of al-balāghah in al-Risālah’s articles in both quantitative and qualitative terms through the employment of DH tools for data mining. By mapping the ontological shifts that occurred in traditional languages and concepts’ employment as both contrastive and continuous, the present inquiries showcase modern Arab culture’s dialectics of secularism and literary revisionism as self-sufficient, culturally embedded and integrated, and not as byproducts of contact with the West.

Election Voting Trend Prediction System

This paper presents the use of Bayesian networks and K-Nearest Neighbor algorithms for predicting election results. Our motivation stemmed from the complexities of the election data available, which spans over 120,000 voting locations across 36 states in Nigeria, and the requirement to develop a procedure that takes into consideration voter trends that are influenced by political parties seeking to win. The system architecture's translation was utilised, and the prototyping methodology was adopted. In order to realize the requirements, the system was designed and implemented using Java and MySQL in accordance with specifications. Since the outcome is positive, it can serve as a benchmark for further study in this field, particularly when it comes to using data mining tools to analyze election results.

An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering

The numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data efficiently. This is the case with Association Rules, a tool for unsupervised data mining that extracts information in the form of IF-THEN patterns. Although various approaches for extracting frequent itemset (prior step before mining association rules) in extremely large databases have been presented, the high computational cost and shortage of memory remain key issues to be addressed while processing enormous data. The objective of this research is to discover frequent itemset by using clustering for preprocessing and adopting the linear prefix tree algorithm for mining the maximal frequent itemset. The performance of the proposed CL-LP-MAX-tree was evaluated by comparing it with the existing FP-max algorithm. Experimentation was performed with the three different standard datasets to record evidence to prove that the proposed CL-LP-MAX-tree algorithms outperform the existing FP-max algorithm in terms of runtime and memory consumption.

Predicting Cold-Stress Responsive Genes in Cotton with Machine Learning Models

Machine Learning (ML) serves as a potent tool for data mining and predictive analytics in genomic research. However, its application in identifying stress-responsive genes remains underexplored. This study identified distinct variations in the expression patterns of one-to-one homologous genes responding to cold stress in three cotton species: Gossypium hirsutum, Gossypium barbadense, and Gossypium arboreum. To better understand cold-responsive genes, we developed ML predictive models (LightGBM, XGBoost, and Random Forest) utilizing 121 biochemical features. The incorporating of these features significantly enhanced model accuracy. Moreover, incorporating evolutionary information further refined the models, achieving an impressive 80.80% accuracy in predicting cold-stress responsive genes. Notably, models trained on sequence features from G. hirsutum showed transferability to the closely related species of G. barbadense, with accuracies ranging from 78.65% to 83.04%. This research presents a promising workflow for identifying candidate genes for experimental exploration of cold stress responses and establishes a systematic framework for predicting cold-stress related genes using ML methodologies.

Intrusion Detection System Application with Machine Learning

Information security holds paramount importance for organizations and users alike, safeguarding against unauthorized access to sensitive data. Daily usage of the internet amplifies the importance of security measures and the detection of malicious activities. Cyber-attacks, as these malicious activities are commonly known, are continually evolving with advancements in hardware, software, and complex network algorithms. Intrusion Detection Systems play a crucial role in shielding data and information from cyberattacks. The rapid progression in machine learning and deep learning, two popular methodologies in data mining, has found applications in various fields, including security. This study focuses on the use of machine learning and deep learning methods to design an intelligent intrusion detection system. For the development of this smart intrusion detection system, two well-established datasets, NSL-KDD and Kyoto 2006+, were employed. Machine learning methods were implemented utilizing the classification algorithms available in the WEKA data mining tool. The results obtained from these classification algorithms were compared with the deep learning model designed within the scope of the study. Consequently, a detailed analysis of machine learning and deep learning methods on the NSL-KDD and Kyoto 2006+ datasets for an intelligent intrusion detection system was conducted, and suggestions were proposed for further research endeavors.

Analyzing Resampling Techniques for Addressing the Class Imbalance in NIDS using SVM with Random Forest Feature Selection

The purpose of Network Intrusion Detection Systems (NIDS) is to ensure and protect computer networks from harmful actions. A major concern in NIDS development is the class imbalance problem, i.e., normal traffic dominates the communication data plane more than intrusion attempts. Such a state of affairs can pose certain hazards to the effectiveness of detection algorithms, including those useful for detecting less frequent but still highly dangerous intrusions. This paper aims to utilize resampling techniques to tackle this problem of class imbalance in NIDS using a Support Vector Machine (SVM) classifier alongside utilizing features selected by Random Forest to improve the feature subset selection process. The analysis highlights the combativeness of each sampling method, offering insights into their efficiency and practicality for real-world applications. Four resampling techniques are analyzed. Such techniques include Synthetic Minority Over-sampling Technique (SMOTE), Random Under-sampling (RUS), Random Over-sampling (ROS) and SMOTE with two different combinations i.e., RUS SMOTE and RUS ROS. Feature selection was done using Random Forest, which was improved by Bayesian methods to create subsets of features with feature rankings determined by Cumulative Feature Importance Score (CFIS). The CIDDS-2017 dataset is used for the performance evaluation, and the metrics used include accuracy, precision, recall, F-measure and CPU time. The algorithm that performs best overall in the CFIS feature subsets is SMOTE, and the features that give the best result are selected at the 90% level with 25 features. This subset accomplishes a relative accuracy enhancement of 0.08% than the other approaches. The RUS+ROS technique is also fine but somehow slower than SMOTE. On the other hand, RUS+SMOTE shows relatively poor results although it consumes less time in terms of computational time compared to other methods, giving about 50% of the performance shown by the other methods. This paper's novelty is adapting the RUS method as a standalone test for screening new and potentially contaminated datasets. The standalone RUS method is more efficient in terms of computations; the algorithm returned the best result of 98.13% accuracy at 85% at the CFIS level of 34 features with a computation time of 137.812 s. It is also noted that SMOTE is considered to be proficient among all resampling techniques used for handling the problem of class imbalance in NIDS, vice 90% CFIS feature subset. Future research directions could include using these techniques in different data sets and other machine learning and deep learning methods together with ROC curve analysis to provide useful pointers to NIDS designers on how to select the right data mining tools and strategies for their projects.

Predictive models for heat stress assessment in Holstein dairy heifers using infrared thermography and machine learning.

Heat stress is a condition that impairs the animal's productive and reproductive performance, and can be monitored by physiological and environmental variables, including body surface temperature, through infrared thermography. The objective of this work is to develop computational models for classification of heat stress from respiratory rate variable in dairy cattle using infrared thermography. The database used for the construction of the models was obtained from 10 weaned heifers, housed in a climate chamber with temperature control, and submitted to thermal comfort and heat wave treatments. Physiological and environmental data were collected, as well as thermographic images. The machine learning modeling environment used was IBM Watson, IBM's cognitive computing services platform, which has several data processing and mining tools. Classifier models for heat stress were evaluated using the confusion matrix metrics and compared to the traditional method based on Temperature and Humidity Index. The best accuracy obtained for classification of the heat stress level was 86.8%, which is comparable to previous works. The authors conclude that it was possible to develop accurate and practical models for real-time monitoring of dairy cattle heat stress.

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<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.

No Association Between Growth Hormone Therapy and Upper Extremity Physeal Tension Injuries: A Matched Case-control Study.

Recent evidence has identified a strong association between growth hormone therapy and physeal injuries in the lower extremity; however, few studies have investigated this association in the upper extremity. (1) Do pediatric patients with physeal tension injuries of the shoulder and elbow have higher odds of having exposure to recombinant growth hormone therapy than matched controls? (2) Are the odds of having exposure to recombinant growth hormone therapy in physeal tension injuries different when stratified by shoulder and elbow injuries? Using a matched case-control study design, patients between 4 and 18 years of age treated at a large, urban, academic center from February 1, 2016, to November 6, 2023, were identified by ICD-10 codes using EPIC SlicerDicer, an electronic medical record-based data mining tool. Patients diagnosed with physeal tension injuries in the shoulder or elbow were included in the case group, and those with midshaft radius, metaphyseal radius, or both-bone forearm fractures were included in the control group. A total of 618 patients with physeal injuries and 1244 with non-physeal fractures were identified and screened for inclusion. After further chart review to confirm diagnoses, 46% (283) of patients with physeal injuries and 54% (670) of patients with non-physeal fractures were included. A further 6% (16) of patients with physeal injuries and 2% (15) of patients with non-physeal injuries were excluded due concomitant dislocations or missing data, resulting in 267 eligible patients with physeal injuries and 655 eligible patients with non-physeal fractures. Two patients with concurrent elbow and shoulder physeal injuries were additionally excluded from stratified analyses. Patients with physeal injuries and non-physeal fractures were 1:1 matched by age ± 0.5 years, sex, and BMI ± 2 kg/m2. In all, 522 patients were included in the analysis, including 261 patients with physeal injuries and 261 with non-physeal fractures. The mean ± SD ages for both patient groups was 13 ± 2 years (p = 0.44), 88% (229 of 261) of all participants were male, and the mean BMIs were 19.9 ± 3.0 kg/m2 and 19.4 ± 3.0 kg/m2 (p = 0.11), respectively. Growth hormone exposure was compared between patients with physeal injuries and non-physeal fractures using a conditional logistic regression model. Overall, 4% (10 of 261) of patients with physeal injuries had exposure to growth hormone therapy as compared with 2% (4 of 261) of patients with non-physeal fractures (OR 2.5 [95% confidence interval 0.8 to 8.0]). Subgroup analyses of shoulder and elbow injuries demonstrated no difference in growth hormone exposure between patients with physeal injuries and non-physeal fractures (OR 2 [95% CI 0.4 to 10.2] and OR 3 [95% CI 0.6 to 14.9], respectively). In light of these results, clinicians may not need to advise precaution against sports or other activities that put the upper extremity physes under stress when treating patients with growth hormone supplementation therapy. Future multicenter studies, however, are indicated to further investigate for the existence of any subtle association between growth hormone therapy and upper extremity physeal injuries relative to the large association previously noted in the lower extremity in pediatric and adolescent patients. Level III, therapeutic study.

Enhancing stroke prediction using the waikato environment for knowledge analysis

<p>State-of-the-art data mining tools incorporate advanced machine learning (ML) and artificial intelligence (AI) models, and it is widely used in classification, association rules, clustering, prediction, and sequential models. Data mining is important for the process of diagnosing and predicting diseases in the early stages, and this contributes greatly to the development of the health services sector. This study utilized classification to predict the stroke of a sample of the patient dataset that was taken from Kaggle. The classification model was created using the data mining program waikato environment for knowledge analysis (WEKA). This data mining tool helped identify individuals most at risk of stroke based on analysis of features extracted from the patient’s dataset. These features were used in classification processes according to the naive Bayes (NB), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. Analysis of the classification results of the previous algorithms showed that the SVM outperformed other algorithms in terms of accuracy (94.4%), sensitivity (100%), and F-measure (97.1%). However, the NB algorithm had the best performance in terms of precision (95.7%).</p>

Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications

The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performance analysis of modifications made to enhance the exact kNN techniques, particularly focusing on kNN Search and kNN Join for high-dimensional data. We delve deep into 31 kNN search methods and 12 kNN join methods, providing a methodological overview and analytical insight into each, emphasizing their strengths, limitations, and applicability. An important feature of our study is the provision of the source code for each of the kNN methods discussed, fostering ease of experimentation and comparative analysis for readers. Motivated by the rising significance of kNN in high-dimensional spaces and a recognized gap in comprehensive surveys on exact kNN techniques, our work seeks to bridge this gap. Additionally, we outline existing challenges and present potential directions for future research in the domain of kNN techniques, offering a holistic guide that amalgamates, compares, and dissects existing methodologies in a coherent manner.Graphical