Data Analytics Methods Research Articles

The Future of Wastewater Treatment Plant: Integrating HCNN-BiGRU-A Model for Superior Performance

Many canals carry wastewater into the community. Chemicals and microorganisms in water supply contamination cause effluent that harms humans and the environment. Unregulated waste water disposal can spread infectious hepatitis, cholera, typhoid, and dysentery. Sanitary waste water disposal protects public health and prevents infectious diseases. Integrated food waste and waste water treatment modeling is efficient for addressing rising food waste. Conventional food waste treatment can produce significant levels of total nitrogen (T-N), which can degrade effluent water quality. Due to their lack of expertise and equipment, operators and engineers struggle to extract usable data from huge databases. Unfortunately, much digital data is never used. In recent years, many data analytics methods have evolved. Methods yield accurate findings on huge datasets. However, these technologies have not been extensively studied for wastewater treatment. To do this, we created a machine learning-enabled water quality analysis and prediction platform. Before using deep learning models, data must be reduced, integrated, purified, and transformed. It uses feature selection to improve qualities. The HCNN-BiGRU-A models predicted best. These findings suggest that ensemble learning models suit nonlinear data better. The HCNN-BiGRU-A model also examined how input factors affected sludge generation. The daily wastewater intake and ambient temperature had the biggest impact. This work is unusual in using ML to estimate wastewater treatment facility sludge production.

Using Laboratory Test Results for Surveillance During a New Outbreak of Acute Hepatitis in 3-Week- to 5-Year-Old Children in the United Kingdom, the Netherlands, Ireland, and Curaçao: Observational Cohort Study.

In March 2022, a concerning rise in cases of unexplained pediatric hepatitis was reported in multiple countries. Cases were defined as acute hepatitis with serum transaminases >500 U/L (aspartate transaminase [AST] or alanine transaminase [ALT]) in children aged 16 years or younger. We explored a simple federated data analytics method to search for evidence of unreported cases using routinely held data. We conducted a pragmatic survey to analyze changes in the proportion of hospitalized children with elevated AST or ALT over time. In addition, we studied the feasibility of using routinely collected clinical laboratory results to detect or follow-up the outbreak of an infectious disease. We explored a simple federated data analytics method to search for evidence of unreported cases using routinely held data. We provided hospitals with a simple computational tool to enable laboratories to share nondisclosive summary-level data. Summary statistics for AST and ALT measurements were collected from the last 10 years across all age groups. Measurements were considered elevated if ALT or AST was >200 U/L. The rate of elevated AST or ALT test for 3-week- to 5-year-olds was compared between a period of interest in which cases of hepatitis were reported (December 1, 2021, to August 31, 2022) and a prepandemic baseline period (January 1, 2012, to December 31, 2019). We calculated a z score, which measures the extent to which the rate for elevated ALT or AST was higher or lower in the period of interest compared to a baseline period, for the 3-week- to 5-year-olds. Our approach of sharing a simple software tool for local use enabled rapid, federated data analysis. A total of 34 hospitals in the United Kingdom, the Netherlands, Ireland, and Curaçao were asked to contribute summary data, and 30 (88%) submitted their data. For all locations combined, the rate of elevated AST or ALT measurements in the period of interest was not elevated (z score=-0.46; P=.64). Results from individual regions were discordant, with a higher rate of elevated AST or ALT values in the Netherlands (z score=4.48; P<.001), driven by results from a single center in Utrecht. We did not observe any clear indication of changes in primary care activity or test results in the same period. Hospital laboratories collect large amounts of data on a daily basis that can potentially be of use for disease surveillance, but these are currently not optimally used. Federated analytics using nondisclosive, summary-level laboratory data sharing was successful, safe, and efficient. The approach holds potential as a tool for pandemic surveillance in future outbreaks. Our findings do not indicate the presence of a broader outbreak of mild hepatitis cases among young children, although there was an increase in elevated AST or ALT values locally in the Netherlands.

Digital Advocacy Strategies with Data Analytics Framework: A Case Study for Effective Campaigns

This paper presents a case study of a digital campaign on social media to formulate an extensive novel framework using data analytics method to measure the effectiveness of a social media campaign. The Social Media Impact Discovery (SMID) framework is built from the concept of digital advocacy strategies and data analytics techniques, which will provide a systematic approach in both domains to carry-out an effective advocacy campaign. Since the advent of social media such as Twitter in providing platforms for freedom of speech, digital advocacy has become the new approach for delivering campaigns to a wider range of targeted audiences. The use of digital advocacy has generated interest among politicians and advocators to spread their messages across. Despite the numerous and widespread use of digital advocacy, there is still no formal framework that study the success of the technique as end outcomes in terms of the technological aspect of knowledge and the data analytic. The impact of the campaign is evaluated in terms of its effectiveness using deep machine learning methods on the acquired data on social media of a digital campaign. The results have shown that continuous loops using Support Vector Machine (SVM) and the Naïve Bayes classifier support the dynamic approach in the SMID framework to increase campaign effect and is in line with the iterative structure. The loop's cyclical structure is in tune with the shifting user trends and social media user behaviours. The framework's ability to include fresh data and modify models as advocacy campaigns change over time provides a tactical advantage responsive to shifting conditions. Thus, the overall processes of the campaign should be supported with accurate and reliable data to build trust among its audiences. A case study of sexual harassment on Twitter is used to assess the SMID framework’s practicality. It centred on a young Malaysian advocate named Ain Husniza, whose notable catchphrase is #MakeSchoolASaferPlace. For the first time, digital advocacy for sexual harassment campaigns has been used and analysed in this study. It has demonstrated its contribution by amplifying survivors’ voices and fostering online solidarity, with the aim of raining public awareness of the issue and promoting informed policy.

Strategies for Effective Product Roadmap Development and Execution in Data Analytics Platforms

Manufacturing is only one of several industries going through a digital transformation in this era of digital disruption. Manufacturing businesses are racing to adopt IoT-based solutions in order to innovate, increase productivity, lower costs, and gain greater market share because of the enormous transformational potential offered by Internet-of-Things (IoT) &amp; Big Data. It is being used by industrial companies all over the world to increase flexibility while achieving cost savings, reduced inefficiencies, and better performance. It is no longer a trend for the future. But putting Industry 4.0 technology support into practice is a tough endeavour that gets increasingly harder in the absence of a common method. These are produced during the course of business operations and are kept in databases, email communication, transaction logs, free form texts on (business) social media, and other places. Businesses want to integrate data analytics methods into their decision-making processes by utilisation of these data. The field of Big Data analyses has seen tremendous progress in the IT sector in recent years. It appears that in order to deal with the extremely dynamic situations of today, new methods for product route mapping are required. This article provides an overview of the literature in science on product road mapping in order to shed light on the current state of the art &amp; pinpoint research gaps. In order to demonstrate the influence of Industry 4.0 technological innovations on the manufacturing sector, this study provides an organised and content-focused evaluation of the literature. This paper offers recommendations for converting a legacy manufacturing facility into an Industry 4.0-compliant smart plant. Research gaps encompass topics including how to include objectives or results in product roadmaps, how to match a roadmap with the item's vision, and how to include activities related to product discovery in product roadmaps.

From Fax to Secure File Transfer Protocol: The 25-Year Evolution of Real-Time Syndromic Surveillance in England.

The purpose of syndromic surveillance is to provide early warning of public health incidents, real-time situational awareness during incidents and emergencies, and reassurance of the lack of impact on the population, particularly during mass gatherings. The United Kingdom Health Security Agency (UKHSA) currently coordinates a real-time syndromic surveillance service that encompasses 6 national syndromic surveillance systems reporting on daily health care usage across England. Each working day, UKHSA analyzes syndromic data from over 200,000 daily patient encounters with the National Health Service, monitoring over 140 unique syndromic indicators, risk assessing over 50 daily statistical exceedances, and taking and recommending public health action on these daily. This English syndromic surveillance service had its origins as a small exploratory pilot in a single region of England in 1999 involving a new pilot telehealth service, initially reporting only on "cold or flu" calls. This pilot showed the value of syndromic surveillance in England, providing advanced warning of the start of seasonal influenza activity over existing laboratory-based surveillance systems. Since this initial pilot, a program of real-time syndromic surveillance has evolved from the single-system, -region, -indicator pilot (using manual data transfer methods) to an all-hazard, multisystem, automated national service. The suite of systems now monitors a wide range of syndromes, from acute respiratory illness to diarrhea to cardiac conditions, and is widely used in routine public health surveillance and for monitoring seasonal respiratory disease and incidents such as the COVID-19 pandemic. Here, we describe the 25-year evolution of the English syndromic surveillance system, focusing on the expansion and improvements in data sources and data management, the technological and digital enablers, and novel methods of data analytics and visualization.

Optimization of Chemical Engineering Processes in the Mining and Metal Industry

Process optimization is an important area of research in the mining and metal industries. The application of mathematical models and optimization techniques has led to significant improvements in process efficiency, reduced operating costs, and improved product quality. The use of simulation tools has also allowed for the development of virtual plants that can be used to test different process scenarios and optimize plant performance. To completely reap the rewards of process optimisation, there are still several issues that need to be resolved. The integration of sustainability and environmental impact assessments into the optimisation process is one of the major issues. This necessitates the creation of models that can take the environmental impact of various process factors into consideration and enable process optimisation using environmental standards. The creation of more complicated mathematical models that can capture the intricate interconnections between various process factors presents another difficulty. Advanced machine learning and data analytics methods like neural networks and genetic algorithms must be used for this. Despite these challenges, the future of process optimization looks promising. Emerging technologies, such as the Internet of Things and big data analytics, are opening up new opportunities for process optimization. The use of sensors and real-time data analytics can provide plant operators with the information they need to make real-time decisions and optimize plant performance. Process optimization is a critical area of research for the mining and metal industries. The use of mathematical models, optimization techniques, and simulation tools has led to significant improvements in process efficiency and product quality.

Enhancing IoT Data Analysis with Machine Learning: A Comprehensive Overview

Machine learning techniques are essential for processing the vast volume of IoT data efficiently, improving performance, and managing IoT applications effectively. Machine learning algorithms play a crucial role in detecting malicious attacks and anomalies in real-time IoT data analysis, thereby enhancing the security of IoT devices. The integration of big data analytics methods with machine learning techniques can further enhance IoT data analysis, improving the performance of IoT applications and overcoming related challenges. Real-time data collection using sensors like DHT11 and Gas level sensors, coupled with machine learning algorithms, enables efficient analysis of IoT data, aiding in the identification of anomalies and attacks. The comprehensive overview of enhancing IoT data analysis with machine learning provides insights for future research, including exploring advanced machine learning algorithms and optimizing data preprocessing techniques to enhance IoT data analysis capabilities.

A big data analytics method for assessing creditworthiness of SMEs: fuzzy equifinality relationships analysis

A big data analytics method for assessing creditworthiness of SMEs: fuzzy equifinality relationships analysis

Predictive Analysis for Personalized Machine: Leveraging Patient Data for Enhanced Healthcare

This research explores predictive analysis for personalized machine: leveraging patient data for enhanced healthcare. By leveraging the power of information and analytics, the healthcare industry can be driven towards a more patient-centric, proactive model that enhances outcomes and improve the overall quality of care. The objectives of the study are to: determine the significance and challenges of predictive analytics in healthcare, ascertain the data analytics techniques used in healthcare to enhance patient care, find out how predictive analytics can be applied for enhanced healthcare, and determine the ethical considerations associated with healthcare predictive analytics. This study employs the case study approach and experimental design. The study analyzes case studies of real-time deployment of predictive analytics models in healthcare centers, examines how these models enhance the healthcare delivery in those centers. Experiments were also conducted to understand how predictive analytics works. The C4.5 learning algorithm was employed to predict the presence of chronic kidney disease (CKD) in patients and differentiate between those not affected by the condition. The C4.5 classifier shows reasonable strength, evident in the large number of rightly classified occurrences (396) and a low misclassification of only 4 occurrences. This is further demonstrated by a low error rate of 0.37, as shown in table 5. The prevalence of this algorithm is emphasized by the large value of KS (0.97), indicating the classifier’s ground-breaking accuracy and performance. The performance of C4.5, featured by its minimal execution time and accuracy, puts it as a decent classifier. This characteristic makes it specifically well-suited for application in the healthcare sector, particularly for tasks involving prediction and classification. The application of data analytics methods for predictive analysis holds significant benefits in the health sector, as it gives us the power to predict and address potential threats to human health, covering different age groups, from the young ones to the elderly. This proactive method enables early disease detection, helping in timely interventions and contributing to better decision-making.

Data analytics in the football industry: a survey investigating operational frameworks and practices in professional clubs and national federations from around the world

ABSTRACT The use of data and analytics in professional football organisations has grown steadily over the last decade. Nevertheless, how and whether these advances in sports analytics address the needs of professional football remain unexplored. Practitioners from national federations qualified for the FIFA World Cup Qatar 2022™ and professional football clubs from an international community of practitioners took part in a survey exploring the characteristics of their data analytics infrastructure, their role, and their value for elaborating player monitoring and positional data. Respondents from 29 national federations and 32 professional clubs completed the survey, with response rates of 90.6% and 77.1%, respectively. Summary information highlighted the underemployment of staff with expertise in applied data analytics across organisations. Perceptions regarding analytical capabilities and data governance framework were heterogenous, particularly in the case of national federations. Only a third of national federation respondents (~30%) perceived information on positional data from international sports data analytics providers to be sufficiently clear. The general resourcing limitations, the overall lack of expertise in data analytics methods, and the absence of operational taxonomies for reference performance metrics pose constraints to meaningful knowledge translations from raw data in professional football organisations.

The Use of Data Analytics in Public Administration for Corruption Prevention During Hybrid Warfare

The relevance of the selected theme is stipulated by the destructive effect of corruption on the social structure of society and the weakening of the rule of law. Corruption schemes as an instrument of geopolitics are used as a part of the foreign policy arsenal of authoritarian states such as Russia. Under such circumstances, data analytics provides researchers and public society with a large volume of analytical instruments, enabling comprehensive monitoring of governments. Such monitoring enhances the efficiency of corruption prevention in public administration. The article aims to determine the data analytics method for corruption prevention in public administration during hybrid warfare. Using the methods of content analysis, historical, abstract-logical methods, we found how data analytics can contribute to effective corruption prevention in public administration. Perspective and appropriate methods of data analytics used for corruption prevention in public administration were found. Based on the results of the conducted study, recommendations for improvement of the use of data analytics in public administration for corruption prevention during hybrid warfare were offered. The perspective of further studies includes research on introducing data analytics in the everyday work of particular state institutions.

A big data analytics method for the evaluation of maritime traffic safety using automatic identification system data

The complex traffic situations are among the factors influencing maritime safety. They can be quantitatively estimated through the analysis of traffic data. This paper explores the impact of complex traffic situations on maritime safety, focusing on inland waterway traffic. It presents a big data analytics method, utilizing data from the Automatic Identification System (AIS) and historical maritime accident records. The methodology involves AIS data preprocessing and spatial autocorrelation models, including Moran's index, to extract and evaluate the dynamic characteristics of maritime traffic. The analysis of traffic characteristic includes a thorough investigation into the spatial-temporal distribution of ship average speed and trajectory density. The paper then introduces an effective traffic characteristic analysis model that evaluates the relationship between maritime traffic patterns and accidents. The study, specifically targeting the Nanjing section of the Yangtze River, reveals variations in ship trajectory density and average speed over time. It identifies several hotspots with a significant local correlation between these factors. Moreover, a substantial correlation is found between the locations of maritime accidents and areas with increased ship trajectory density and average speed. These results may provide insights for traffic safety management and highlight strategies for preventing maritime accidents.

Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion

Digital trades and payments are becoming increasingly popular, as they typically entail monetary transactions. This not only makes electronic transactions more convenient for the end customer, but it also raises the likelihood of fraud. An adequate fraud detection system with a cutting-edge model is critical to minimizing fraud costs. Identifying fraud at the ideal time entails establishing and setting up ubiquitous systems to consume and analyze massive amounts of streaming data. Recent advances in data analytics methods and introducing open-source technology for big data storage and processing opened new options for detecting fraud. This study aims to tackle this critical issue by providing a newly real-time e-transaction fraud detection schema that consolidates the advantages of both unsupervised learners, including autoencoder and extended isolation forests, with cutting-edge big data gadgets such as Spark streaming and sparkling water. It addresses the shortage of non-fraudulent instances and handles the excessive dimension of the set of features. On two real-world transactional datasets, we assess our suggested technique. Compared with other current fraud identification systems, our methodology delivers an elevated accuracy yield of 99%. Furthermore, it outperforms state-of-the-art approaches in reliably identifying fraudulent samples. Doi: 10.28991/HIJ-2024-05-01-014 Full Text: PDF

Efficiency evaluation of China's transportation system considering carbon emissions: Evidence from big data analytics methods

China's transportation sector is a vital link between production and consumption, but it also has issues with low efficiency, high carbon emissions, and technological bottlenecks. To improve efficiency and provide actionable recommendations and strategies, this study first constructs a comprehensive evaluation index system to gauge the transportation sector's inputs using panel data from different Chinese provinces from 2007 to 2021. Within the assessment system, the principal component analysis (PCA) method is used to reduce the dimension of the indexes, thereby yielding a set of adjusted inputs. Subsequently, the transportation system efficiency (TSE) is evaluated using the super-efficiency SBM-DEA model, which includes unexpected outputs such as carbon emissions, and three-stage DEA modifies the efficiency. Then, we calculate the Malmquist-Luenberger index (TML) and its components: technological change (TTC) and technological efficiency change (TEC). Lastly, the influential factors impacting TSE are analyzed via a truncated regression Tobit model. The following are the conclusions: (1) The transportation industry in China exhibits inefficiency, and the average TSE in Stage I and III is 0.91 and 0.93, respectively. TSE is underestimated due to the influence of external environmental factors and inefficiencies in management in Stage I. (2) TSE in the eastern area also produces significant carbon emissions that surpass the national average. At the same time, other regions face efficiency limitations due to geographical constraints and management obstacles. (3) Insufficient technical capacity is a major cause of inefficiency in the transport sector and is prevalent in the northeast, west, and central regions. (4) Population growth and income per capita advancements foster transportation industry development, while increased GDP, fiscal revenues, and traffic accidents contribute to declining efficiency. The study above findings serve as a foundation for regional and national management initiatives and policies to enhance transportation effectiveness.

Understanding the University Student Experience Through Big Data Analytics

Despite best efforts, the student experience remains poorly understood. One under-explored approach to understanding the student experience is the use of big data analytics. The reported study is a work in progress aimed at exploring the value of big data methods for understanding the student experience. A big data analysis of an open dataset of student comments is being undertaken. The first and simplest use of big data analytics is for the identification of high frequency keyword groups, which, without big data analytics, would be extremely time consuming. However, the lack of context surrounding keyword groups severely limited the ability to draw meaningful conclusions and highlighted the need for human intervention in the analysis process. Future work includes sentiment analysis. This initial work is an impetus for further exploration of big data analytics methods in qualitative contexts, especially in dynamic contexts where rapid data analysis can form a basis for timely interventions.

Assessment of Türkiye's Digitalization Performance within the Framework of the UN Sustainable Development Index

The aim of this study is to examine the relationship between the accelerated digitalization process in Türkiye in the 2000s and the developments in the Sustainable Development Goals (SDG) index observed in the same period. The main question motivating the study is the nature of the effects of increasing digitalization on sustainable development. Quantitative data analytics methods were used in the study. Time series data on digitalization and SDG index for Türkiye were collected and descriptive and inferential statistical analyses were applied. The findings show that there has been a significant increase in digitalization indicators in Türkiye since the 2000s. In the same period, an increase was also observed in the SDG index. Although a positive relationship was found between the two variables, it was understood that more detailed analyses are needed to make a definitive judgment in terms of causality. The study is important in terms of raising awareness of the relationship between digitalization and sustainable development in Türkiye and guiding future research.

IoT and Cloud Based Sustainable Smart Irrigation System

This paper addresses the concerns in irrigation mechanisms and employs a Sensors and cloud-based platform to monitor and control an irrigation system. In order to monitor the soil’s moisture level in real time, moisture sensors are placed in the field. These sensors wirelessly provide data to the NodeMCU, which processes and relays the information. The NodeMCU gathers the sensor data and processes it, considering standard deviations and crop-specific parameters. Using this information, the system starts the water pump and opens the solenoid valves to begin the irrigation operation. The NodeMCU coordinates with these valve-opening devices via wireless communication. In addition, the system uses online weather prediction data to provide more precise watering schedule optimisation. The system dynamically modifies the irrigation schedule based on analysis of weather patterns, evapotranspiration rates, and crop water needs to save water during times of rain or high humidity. The solution makes use of cloud-based platforms to improve scalability and accessibility. The gathered sensor data and command instructions are safely transferred to the cloud server. This allows farmers to remotely check on and adjust the irrigation system by means of the web or mobile apps. In addition, data analytics methods may be used to infer information and provide suggestions for improved methods of water management and crop care. In conclusion, the Internet of Things (IoT) NodeMCU smart irrigation system provides an automated and intelligent approach to water management in agriculture. Water is saved, crop yields are boosted, and sustainability is enhanced thanks to the capacity to irrigate precisely based on real-time soil moisture data, weather predictions, and crop needs.

A big data analytics method for the influence of WeChat public platform articles on blood donation behavior

PurposeThe purpose of this paper is to explore the impact and mechanism of WeChat public platforms articles (abbreviated as WPP) on blood donation behavior using data of WPPA and donation behavior data.Design/methodology/approachThis paper uses multiple linear regression methods, web crawlers and natural language processing technology. It first quantifies the impact of WPP published articles on donation behavior. On this basis, it then selects data from the day of article publication to further study the impact of article dissemination on donation behavior from the perspective of reading quantity, and analyzes the influencing factors of article reading quantity.FindingsThe results show that on the same day that an article is published, there is an increase of 13.8 and 14.3% in blood donation volume and fan registrations, respectively. The mediating effect exists. However, the day after an article is published, there is no longer any effect on blood donations. With a 1% increase in reading quantity, blood donation volume on the day of article publication increases by 0.13%, and this positive impact is promoted by the quality of the articles. A conc ise articles title and body and rich images help drive reading quantity. Moreover, blood donors prefer to read articles about blood dynamics and donation promotion, while articles about news, announcements and administrative affairs make them less inclined to read.Originality/valueFirst, it focuses on WPPA, quantifies the impact of articles on blood donation behavior and analyzes the mechanism. Second, the authors study the impact and timeliness of social media article dissemination to address the insufficiency of existing research. Third, the study provides a scientific basis for the editing and publishing of articles, helping blood banks improve the effectiveness of publicity and recruitment.

RELEVANCE OF DATA ANALYTICS IN SUSTAINABLE FISHERIES MANAGEMENT: AN EVIDENCE-BASED STUDY

Aim of the study: The value of fish resources to socioeconomic development is immense. However, there is growing evidence that loss of quality in the marine environments e.g. marine waters at sea and coastal freshwater zones is threatening the availability of livelihood resources e.g. fish resources evidenced by the dwindling fisheries resources that promote sustainable livelihood. Cognizant of this, several fisheries institutions, governments, and local users have come up with several strategies to boost marine water environment management and promote sustainable fishing. Unfortunately, most of the initiatives have not fully managed to repulse unsustainable practices e.g. marine water pollution that leads to loss of wild catches and leads to unsustainable fishing practices. Material and methods: Owing to this we use a mixed research methods approach involving a systematic literature review and field survey in Alappad to argue and present a case for governments and fishery management organizations to invest in data analytics as a new pathway that can give a comprehensive, near real-time view of both ocean resources and coastal fishing activities in light of the declining fish stocks and escalating environmental problems e.g. marine water quality loss e.g. due to pollution. Results and conclusions: Findings documented that by using data analytics, governments, and fisheries management organizations/individuals may respond more swiftly to external forces like climate change, and marine water quality loss and implement new policies and regulations thus promoting sustainable fisheries management. Local perspectives of coastal communities could also guide the best data analytics tool or method to be used.

DEVELOPMENT OF HEALTH INSURANCE CLAIM PREDICTION METHOD BASED ON SUPPORT VECTOR MACHINE AND BAT ALGORITHM

Health insurance industry is very much needed by the community in handling the financial risks in the health sector. The number of claims greatly affects the achievement of profits and the sustainability of the health insurance industry. Therefore, filing claims by insurance users from year to year is important to be predicted in insurance firm. The Machine Learning (ML) method promises to be a good solution for predicting health insurance claims compared to conventional data analytics methods. Support Vector Machine (SVM) is one of the superior ML approaches. Nonetheless, SVM performance is controlled by the suitable selection of SVM parameters. The SVM parameters is typically selected by trial and error, sometimes resulting in not optimal performance and taking a long time to complete. Swarm intelligence-based algorithms can be used to select the best parameters from SVM. This method is capable of locating the global best solution, is simple to implemented, and doesn't involve derivatives. One of the best swarm intelligence algorithms is the Bat Algorithm (BA). BA has a faster convergence rate than other algorithms, for example Particle Swarm Optimization (PSO). Based on this situation, this paper offers the new classification model for predicting health insurance claim based on SVM and BA. The metrics utilized for evaluation are accuracy, recall, precision, f1-score, and computing time. The experimental outcomes show that the proposed approach is superior to the conventional SVM and the hybrid of SVM and PSO in forecasting health insurance claims. In addition, the proposed method has a substantially shorter computing time than the hybrid of SVM and PSO. The outcomes of the experiments also indicate that the new classification model for predicting health insurance claim based on the SVM and BA can avoid over-fitting condition.