Hasil untuk "Computer applications to medicine. Medical informatics"

T. Brunyé, S. Mitroff, Joann G. Elmore

OBJECTIVES Artificial intelligence (AI) has the potential to transform medical informatics by supporting clinical decision-making, reducing diagnostic errors, and improving workflows and efficiency. However, successful integration of AI-based decision support systems depends on careful consideration of human-AI collaboration, trust, skill maintenance, and automation bias. This work proposes five central questions to guide future research in medical informatics and human-computer interface (HCI). MATERIALS AND METHODS We focus on AI-based clinical decision support systems, including computer vision algorithms for medical imaging (radiology, pathology), natural language processing for structured and unstructured electronic health record (EHR) data, and rule-based systems. Relevant data modalities include clinician-acquired images, EHR text, and increasingly, patient-generated content in telehealth contexts. We review existing evidence regarding diagnostic errors across specialties, the effectiveness and risks of AI tools in reducing perceptual and interpretive errors, and the human factors influencing diagnostic decision-making in AI-enabled contexts. We synthesize insights from medicine, cognitive science, and HCI to identify gaps in knowledge and propose five key questions for continued research. RESULTS Diagnostic errors remain common across medicine, with AI offering potential to reduce both perceptual and interpretive errors. However, the impact of AI depends critically on how and when information is presented. Studies indicate that delayed or toggleable cues may outperform immediate ones, but attentional capture, overreliance, and bias remain significant risks. Explainable AI provides transparency but can also bias decisions. Long-term reliance on AI may erode clinician skills, particularly for trainees and in low-prevalence contexts. Historical failures of computer-aided diagnosis in mammography highlight these challenges. DISCUSSION AND CONCLUSION Effective AI integration requires human-centered and adaptive design. Five central research questions address: (1) what type and format of information AI should provide; (2) when information should be presented; (3) how explainable AI affects diagnostic decisions; (4) how AI influences automation bias and complacency; and (5) the risks of skill decay due to reliance on AI. Each question underscores the importance of balancing efficiency, accuracy, and clinician expertise while mitigating bias and skill degradation. AI holds promise for improving diagnostic accuracy and efficiency, but realizing its potential requires post-deployment evaluation, equitable access, clinician oversight, and targeted training. AI must complement, rather than replace, human expertise, ensuring safe, effective, and sustainable integration into diagnostic decision-making. Addressing these challenges proactively can maximize AI's potential across healthcare and other high-stakes domains.

Mina M. Fahim, Richard P. Golonka, Robin L. Walker et al.

Background: The COVID-19 pandemic disrupted primary health care systems worldwide, prompting rapid changes in how care was delivered. In Alberta, this included a significant shift from in-person to virtual care. This study examines trends in primary care utilization among Albertans during COVID-19 and the shift toward virtual care. Methods: Repeated cross-sectional analyses were conducted from 2018/19 to 2022/23 using Alberta Health Practitioner Claims data. Utilization was measured as the proportion of Albertans with at least one visit and the annual visit rate per person. Annual percent change (APC) was calculated relative to the pre-pandemic year (2019/20) and stratified by demographics. Findings: The proportion of Albertans with a primary care visit decreased by −9.55% in 2020/21 but recovered to −4.62% by 2022/23. Annual visit rates remained stable post-pandemic. The largest declines in 2020/21 were among children aged 5 to 11 (−38.42%), ≤4 (−33.42%), newborns (−30.36% to −25.49%), and those without health conditions (−20.9%). Virtual care accounted for 23.77% of visits in 2020/21, dropping to 14.43% by 2022/23. Conclusions: While fewer Albertans accessed primary care, visit rates remained stable due to virtual care. Further research is needed to assess the long-term impacts of COVID-19 on primary healthcare delivery.

Pratya Nuankaew, Thapanapong Sararat, W. Nuankaew

This systematic review critically examines the pivotal role of applied informatics in advancing medical innovation, with a particular focus on the integration of artificial intelligence (AI) and machine learning (ML) technologies. By bringing together recent research, the study demonstrates how these computer tools can transform healthcare, particularly by enhancing the accuracy of illness diagnosis using advanced medical imaging and enabling real-time patient monitoring. New trends in the field indicate that deep learning (DL), the Internet of Things (IoT), and intelligent computer systems are being increasingly utilized, all contributing to enhanced patient care and the development of more effective healthcare systems based on data. The review also examines foundational enablers for sustainable innovation, including the standardization of medical data formats, interoperability across health information systems, and the implementation of robust cybersecurity protocols to safeguard patient privacy and ensure data integrity. While the integration of AI and ML is primarily perceived as beneficial within the healthcare domain, the review identifies several persistent challenges. These include issues of clinician trust in algorithmic decision-making, the need for ethically sound implementation practices, and the development of evolving regulatory frameworks to accommodate rapid technological change. Additionally, the application of ML and data mining in predicting outcomes and aiding clinical decisions shows enormous potential, which could transform how we approach preventive and personalized medicine.

Sanaz Vahdati, Bardia Khosravi, Elham Mahmoudi et al.

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora of available tools for data curation in different stages, it is crucial to stay informed about the most relevant tools within specific research areas. In the current work, we propose a descriptive outline for different steps of data curation while we furnish compilations of tools collected from a survey applied among members of the Society of Imaging Informatics (SIIM) for each of these stages. This collection has the potential to enhance the decision-making process for researchers as they select the most appropriate tool for their specific tasks.

Laura Heermann Langford, Kate Fultz Hollis, Margo Edmunds et al.

Abstract Background  Over the past 30 years, the American Medical Informatics Association (AMIA) has played a pivotal role in fostering a collaborative community for professionals in biomedical and health informatics. As an interdisciplinary association, AMIA brings together individuals with clinical, research, and computer expertise and emphasizes the use of data to enhance biomedical research and clinical work. The need for a recognition program within AMIA, acknowledging applied informatics skills by members, led to the establishment of the Fellows of AMIA (FAMIA) Recognition Program in 2018. Objectives  To outline the evolution of the FAMIA program and shed light on its origins, development, and impact. This report explores factors that led to the establishment of FAMIA, considerations affecting its development, and the objectives FAMIA seeks to achieve within the broader context of AMIA. Methods  The development of FAMIA is examined through a historical lens, encompassing key milestones, discussions, and decisions that shaped the program. Insights into the formation of FAMIA were gathered through discussions within AMIA membership and leadership, including proposals, board-level discussions, and the involvement of key stakeholders. Additionally, the report outlines criteria for FAMIA eligibility and the pathways available for recognition, namely the Certification Pathway and the Long-Term Experience Pathway. Results  The FAMIA program has inducted five classes, totaling 602 fellows. An overview of disciplines, roles, and application pathways for FAMIA members is provided. A comparative analysis with other fellow recognition programs in related fields showcases the unique features and contributions of FAMIA in acknowledging applied informatics. Conclusion  Now in its sixth year, FAMIA acknowledges the growing influence of applied informatics within health information professionals, recognizing individuals with experience, training, and a commitment to the highest level of applied informatics and the science associated with it.

Wenzhong Zhang, Hong Ji, Yan Wu et al.

Background The number of patients undergoing joint replacement procedures is continuously increasing. Tele-equipment is progressively being employed for postrehabilitation of total hip and knee replacements. Gaining a comprehensive understanding of the experiences and requirements of patients undergoing total hip and knee arthroplasty who participate in telerehabilitation can contribute to the enhancement of telerehabilitation programs and the overall rehabilitation and care provided to this specific population. Objective To explore the needs and experiences of total hip and knee arthroplasty patients with telerehabilitation. Design Systematic review and qualitative synthesis. Methods Electronic databases PubMed, Web of Science, The Cochrane Library, Embase, CINAHL, Scopus, ProQuest, CNKI, Wanfang Data, VIP, and SinoMed were systematically searched for information on the needs and experiences of telerehabilitation for patients with total hip arthroplasty and total knee arthroplasty in qualitative studies. The search period was from the creation of the database to March 2024. Literature quality was assessed using the 2016 edition of the Australian Joanna Briggs Institute Centre for Evidence-Based Health Care Quality Assessment Criteria for Qualitative Research. A pooled integration approach was used to integrate the findings inductively. Results A total of 11 studies were included and 4 themes were identified: the desire to communicate and the need to acquire knowledge; accessible, high-quality rehabilitation services; positive psychological experiences; the dilemmas of participating in telerehabilitation. Conclusions This study's findings emphasize that the practical needs and challenges of total hip and knee arthroplasty patients’ participation in telerehabilitation should be continuously focused on, and the advantages of telerehabilitation should be continuously strengthened to guarantee the continuity of patients’ postoperative rehabilitation and to promote their postoperative recovery.

Pietro Elias Fubini, Georges Louis Savoldelli, Tal Sara Beckmann et al.

BackgroundLocal anesthetics (LAs) are regularly used to alleviate pain during medical or surgical procedures. Their use is generally considered safe, but exceeding the maximum recommended doses can lead to LA systemic toxicity, a rare but potentially lethal complication. Determining maximum safe doses is therefore mandatory before performing local anesthesia, but rules are often unclear and the factors affecting dose calculation are numerous. Mobile health apps have been shown to help clinical decision-making, but most currently available apps present significant limitations. The Local Anesthetics Dose Calculator (LoAD Calc) app was designed to overcome these limitations by taking all relevant parameters into account. Before deploying this app in a clinical setting, it should be tested to determine its effectiveness and whether clinicians would be willing to use it. ObjectiveThe primary objective will be to evaluate the effectiveness of the LoAD Calc app through written simulated cases. The secondary objective will be to determine whether physicians find this app easier, faster, and safer than the methods they generally use. MethodsWe describe a parallel-group randomized controlled trial protocol. Anesthesiologists working at the Geneva University Hospitals will be invited to participate. Participants will be asked to compute the maximum dose of LA in 10 simulated clinical cases using 3 different LAs. The maximum safe dose will be determined manually using the same calculation rules that were used to develop LoAD Calc, without using the app itself. An overdose will be considered any dose higher than the correct dose, rounded to the superior integer, while an underdose will be defined as the optimal calculated dose minus 20%, rounded to the inferior integer. Randomization will be stratified according to current position (resident vs registrar). The participants allocated to the LoAD Calc (experimental) group will use the LoAD Calc app to compute the maximum safe LA doses. Those allocated to the control group will be asked to use the method they generally use. The primary outcome will be the overall overdose rate. Secondary outcomes will include the overdose rate according to ideal and actual body weight and to each specific LA, the overall underdose rate, and the time taken to complete these calculations. The app’s usability will also be assessed. ResultsA sample size of 46 participants will be needed to detect a difference of 10% with a power of 90%. Thus, a target of 50 participants was set to allow for attrition and exclusion criteria. We expect recruitment to begin during the winter of 2023, data analysis in the spring of 2024, and results by the end of 2024. ConclusionsThis study should determine whether LoAD Calc, a mobile health app designed to compute maximum safe LA doses, is safer and more efficient than traditional LA calculation methods. International Registered Report Identifier (IRRID)PRR1-10.2196/53679

R. Bellazzi, M. Cecconi, M. Costantino et al.

BACKGROUND Given the impact of bioengineering and medical informatics technologies in health care, the design and implementation of education programs able to combine medical curricula with a proper teaching on engineering and informatics is now of paramount importance. In Italy, this goal has to fit in with the existing higher education system, which is structured into Bachelor programs and Master programs. Medicine and Surgery programs, instead, are designed as a six-year single-cycle Degree Program in Medicine and Surgery which comprises both class attendance and hospital internship and training. This program allows students to become Medical Doctors (MD). The different organization of this University program makes it not easy to introduce further contents, namely hard science courses, in the educational program. Notwithstanding this, we present here some recent innovative programs aimed at widening MD curriculum by including biomedical engineering and informatics subjects. In particular, we will introduce three of them. Two are joint-degree programs, the first between Humanitas University and Politecnico di Milano (MEDTEC School), and the second between University of Calabria and University Magna Graecia of Catanzaro (Medicina e Chirurgia TD). The Third one is a Professional Master coupled with an MD degree, based on a joint program among Pavia University, Pisa University, the Institute of Advanced studies in Pavia and the Scuola Superiore S. Anna in Pisa (MEET). CONTRIBUTION The paper provides a description of the fundamental design principles of the three above mentioned programs, and explores some aspects of the teaching modules, highlighting their positive aspects. In particular, we show how the three different programs allow students to enrich their knowledge by studying engineering subjects and innovative methods and technologies, as well as their applications to patient care. CONCLUSIONS The MEDTEC program is the first degree program at Italian and international scale which integrates medical and engineering subjects. In the following years, other programs were issued in Italy, defining similar education programs to couple a degree in medicine education with bioengineering and medical informatics, among which Medicina e Chirurgia TD and MEET. We believe the experiences described here in this paper represent the possibility of bridging the gap between medical and technological competencies.

Murat Sariyar

The aim of this paper is to investigate whether and how medical informatics can claim to have a sound scientific basis. Why is such clarification fruitful? First, it provides a common ground for the core principles, theories and methods used to gain knowledge and to guide the practice. Without such a ground, medical informatics might be subsumed to medical engineering at one institution and to life sciences at another institution or might be just regarded as an application domain within computer science. We will provide a succinct outline of the philosophy of science, after which we provide an application of the related notions in order to decide the scientific status of medical informatics. We justify viewing medical informatics as an interdisciplinary field with a paradigm that can be formulated as "user-centered process-orientation in the healthcare setting". Even if MI is not merely applied computer science, it still remains uncertain whether it will attain the status of a mature science, especially without comprehensive theories.

Alireza Kasaie, Suchithra Rajendran

This study addresses patient unpunctuality, a major concern affecting patient waiting time, resource utilization, and quality of care. We develop and compare four machine learning models, including multinomial logistic regression, decision tree, random forest, and artificial neural network, to accurately predict patient arrival patterns and aid efficient scheduling. These models are analyzed using the explainable artificial intelligence approach and the Shapley additive explanations model, promoting comprehension and trust in our algorithmic results. Using three years of appointment data from a psychiatric clinic, we identify the travel distance, appointment lead time, patient’s age, Body Mass Index (BMI), and certain mental diagnoses as significant factors affecting the patient’s unpunctuality. Despite the good predictive potential of machine learning algorithms, no single model excels in all performance metrics. The study proposes implementing these machine learning techniques and the explainable artificial intelligence tool into the clinic’s appointment system as a decision support system to minimize patient unpunctuality.

Yongtai Liu, Zhijun Yin, Congning Ni et al.

BackgroundBy the end of 2022, more than 100 million people were infected with COVID-19 in the United States, and the cumulative death rate in rural areas (383.5/100,000) was much higher than in urban areas (280.1/100,000). As the pandemic spread, people used social media platforms to express their opinions and concerns about COVID-19–related topics. ObjectiveThis study aimed to (1) identify the primary COVID-19–related topics in the contiguous United States communicated over Twitter and (2) compare the sentiments urban and rural users expressed about these topics. MethodsWe collected tweets containing geolocation data from May 2020 to January 2022 in the contiguous United States. We relied on the tweets’ geolocations to determine if their authors were in an urban or rural setting. We trained multiple word2vec models with several corpora of tweets based on geospatial and timing information. Using a word2vec model built on all tweets, we identified hashtags relevant to COVID-19 and performed hashtag clustering to obtain related topics. We then ran an inference analysis for urban and rural sentiments with respect to the topics based on the similarity between topic hashtags and opinion adjectives in the corresponding urban and rural word2vec models. Finally, we analyzed the temporal trend in sentiments using monthly word2vec models. ResultsWe created a corpus of 407 million tweets, 350 million (86%) of which were posted by users in urban areas, while 18 million (4.4%) were posted by users in rural areas. There were 2666 hashtags related to COVID-19, which clustered into 20 topics. Rural users expressed stronger negative sentiments than urban users about COVID-19 prevention strategies and vaccination (P<.001). Moreover, there was a clear political divide in the perception of politicians by urban and rural users; these users communicated stronger negative sentiments about Republican and Democratic politicians, respectively (P<.001). Regarding misinformation and conspiracy theories, urban users exhibited stronger negative sentiments about the “covidiots” and “China virus” topics, while rural users exhibited stronger negative sentiments about the “Dr. Fauci” and “plandemic” topics. Finally, we observed that urban users’ sentiments about the economy appeared to transition from negative to positive in late 2021, which was in line with the US economic recovery. ConclusionsThis study demonstrates there is a statistically significant difference in the sentiments of urban and rural Twitter users regarding a wide range of COVID-19–related topics. This suggests that social media can be relied upon to monitor public sentiment during pandemics in disparate types of regions. This may assist in the geographically targeted deployment of epidemic prevention and management efforts.

Jimena Olveres, Germán González, Fabián Torres et al.

Computer vision and artificial intelligence applications in medicine are becoming increasingly important day by day, especially in the field of image technology. In this paper we cover different artificial intelligence advances that tackle some of the most important worldwide medical problems such as cardiology, cancer, dermatology, neurodegenerative disorders, respiratory problems, and gastroenterology. We show how both areas have resulted in a large variety of methods that range from enhancement, detection, segmentation and characterizations of anatomical structures and lesions to complete systems that automatically identify and classify several diseases in order to aid clinical diagnosis and treatment. Different imaging modalities such as computer tomography, magnetic resonance, radiography, ultrasound, dermoscopy and microscopy offer multiple opportunities to build automatic systems that help medical diagnosis, taking advantage of their own physical nature. However, these imaging modalities also impose important limitations to the design of automatic image analysis systems for diagnosis aid due to their inherent characteristics such as signal to noise ratio, contrast and resolutions in time, space and wavelength. Finally, we discuss future trends and challenges that computer vision and artificial intelligence must face in the coming years in order to build systems that are able to solve more complex problems that assist medical diagnosis.

Fabio Sandomenico, Luca Sanduzzi, Emilia La Verde et al.

Acute cholecystitis stands out as one of the most common surgical pathologies that should always be considered in a right-upper abdominal pain emergency. For this, the importance of a correct diagnosis is well described. However, it has been demonstrated that the simple combination of clinical (pain, Murphy’s sign) and laboratory (leukocytosis) parameters alone does not provide for ruling in or ruling out the diagnosis of this condition, unless accompanied by a radiological exam. For a long time, and still today, ultrasonography (US) is by far the first-to-proceed radiologic exam to perform, thanks to its rapidity and very high sensibility and specificity for the diagnosis of simple acute cholecystitis. However, acute cholecystitis can undergo some complications that US struggles to find. In addition to that, studies suggest that multidetector computed tomography (MDCT) is superior in showing complicated forms of cholecystitis in relation to sensibility and specificity and for its capability of reformatting multiplanar (MPR) reconstructions that give a more detailed view of complications. They have shown to be useful for a precise evaluation of vascular complications, the anatomy of the biliary tree, and the extension of inflammation to surrounding structures (i.e., colitis). Therefore, based also on our experience, in patients with atypical presentation, or in cases with high suspicion for a complicated form, a MDCT abdomen scan is performed. In this review, the principal findings are listed and described to create a CT classification of acute complications based on anatomical and topographic criteria.

Jérôme Baranger, Olivier Villemain, Matthias Wagner et al.

Abnormal variations of the neonatal brain perfusion can result in long-term neurodevelopmental consequences and cerebral perfusion imaging can play an important role in diagnostic and therapeutic decision-making. To identify at-risk situations, perfusion imaging of the neonatal brain must accurately evaluate both regional and global perfusion. To date, neonatal cerebral perfusion assessment remains challenging. The available modalities such as magnetic resonance imaging (MRI), ultrasound imaging, computed tomography (CT), near-infrared spectroscopy or nuclear imaging have multiple compromises and limitations. Several promising methods are being developed to achieve better diagnostic accuracy and higher robustness, in particular using advanced MRI and ultrasound techniques.The objective of this state-of-the-art review is to analyze the methodology and challenges of neonatal brain perfusion imaging, to describe the currently available modalities, and to outline future perspectives.

Md. Rakhibul Hasan, Surid Mohammad Chowdhury, Md. Abdul Aziz et al.

In this in silico study, thirty-five ciprofloxacin analogs were docked to the active site of DNA gyrase, the prime target of ciprofloxacin type antibiotics. Prior to docking all the structures were optimized using MM2 force field parameters. The study revealed that five candidates, namely 6MePQ_3, 6MePQ_11, A6MePQ_3, HPQ_11, and PQ_7, exhibited promising binding to DNA gyrase. Upon analysis of the ligand-receptor complex, it is also divulged that this binding has been stabilized by the interaction between different neutral, nonpolar, aromatic amino-acid residues of DNA gyrase and the ciprofloxacin analogs. Moreover, the interaction between ciprofloxacin analogs and DNA gyrase was mainly governed by hydrophobic interactions and, to a lesser extent, hydrogen bonds. Halogen bonds, electrostatic interactions, and other types of interactions were almost absent in all cases. Molecular dynamic simulation was performed to recognize the structural variations and the complexes' stability of suggested ligands. This study indicates that 6MePQ_3 forms a stable drug-protein complex. On the other site, Pharmacokinetic filtering done using SwissADME server, reveals that 6MePQ_3 is well absorbed from the GI tract, does not cross BBB and is not a P-gp substrate. But it is possible to check and confirm its all “in silico” pharmacodynamics and pharmacokinetics characteristics in real life by synthesis and subsequent analysis of this ligand.

Siste, Kristiana, Hanafi, Enjeline, Sen, Lee Thung et al.

Yun Chen Dr, Ernst Ginell Dr

Due to the advent of technology in the medical sector, the future of the Computer-Aided Decision (CAD) is promising as a support system for the processing of images. Since there is significant results reported due to the application of diagnostic radiology in the healthcare facility, radiologists are looking forward to enhancing medical and bioinformatics using CAD. Medical evaluations and trials have been done over the past few decades to aid in the optimization of accurate programs and evaluate the real contributions of CAD in the medical informatics interpretation procedures. Health experts and radiologists utilizing patient outputs from fundamental application of CAD are placed in the best position to focus on the final decisions concerning the performance of patients and diagnosis. However, researches have shown that the computer outputs require not projecting significant general accuracy compared to a certain radiologist to enhance patients’ performance. The volume and measure of the present patient data including their complexity to enhance the process of making proper healthcare decisions while making it problematic for healthcare practitioners and physicians to facilitate the management of patients. This condition calls for the usage of biomedical informatics methodologies to effectively process information, create biomedical implementations and informatics frameworks for CAD support systems. With that regard, this paper evaluates the medical and bioinformatics based on the application of CAD systems. It further projects on the applications of the systems, their application guidelines and techniques. The paper ends with the analysis of the future problems and directions of the CAD support framework.

Progress H. Nyanga, Bridget Bwalya Umar, Douty Chibamba et al.

A survey was conducted with 235 randomly selected households to investigate women's empowerment, land and donor-driven agricultural interventions in Eastern Zambia [1] for aid programmes with (Norwegian) and without (Chinese) women empowerment goals. The survey was complemented by six focus group discussions (FGDs) and 12 key informant interviews. A triple-stream approach for focus discussions was used (i.e. women-only, men-only, and mixed gender). The results suggest that despite differing aid programme modalities, there was increased access to, and control over, productive resources by women farmers. At least 60% of the respondents reported joint ownership of all types of livestock and poultry, including large livestock such as cattle. Within households, decisions on cotton, groundnuts, and maize were made jointly by the husband and wife. Greater than 70% of the respondents in both Norwegian and Chinese aided households reported joint decision making by the husband and wife. Although both men and women farmers attended training sessions, the percentage of attending respondents was lower for Chinese-aid affiliated farmers. The majority (81% - Norwegian aid; 89% – Chinese aid) jointly earned and owned the income from cotton. When women entered into contract farming with the cotton company, operations management was performed by the entire household, and the applicable income was considered jointly earned.

Robinson Spencer, Fadi Thabtah, Neda Abdelhamid et al.

Machine learning has been used successfully to improve the accuracy of computer-aided diagnosis systems. This paper experimentally assesses the performance of models derived by machine learning techniques by using relevant features chosen by various feature-selection methods. Four commonly used heart disease datasets have been evaluated using principal component analysis, Chi squared testing, ReliefF and symmetrical uncertainty to create distinctive feature sets. Then, a variety of classification algorithms have been used to create models that are then compared to seek the optimal features combinations, to improve the correct prediction of heart conditions. We found the benefits of using feature selection vary depending on the machine learning technique used for the heart datasets we consider. However, the best model we created used a combination of Chi-squared feature selection with the BayesNet algorithm and achieved an accuracy of 85.00% on the considered datasets.