Academia.eduAcademia.edu

Figure 7 – uploaded by Debashis Kundu

See full PDFdownloadDownload figure
Table 2: Blockchain System Performance Metrics Fig 7: Blockchain System Performance 6.4 Performance Evaluation of Machine Learning Models The blockchain system's performance is evaluated based on its ability to ensure data integrity, transparency, and security. Key metrics include transaction throughput, latency, and the number of transactions processed per second.

Table 2 Blockchain System Performance Metrics Fig 7: Blockchain System Performance 6.4 Performance Evaluation of Machine Learning Models The blockchain system's performance is evaluated based on its ability to ensure data integrity, transparency, and security. Key metrics include transaction throughput, latency, and the number of transactions processed per second.

Related Figures (11)

Table 1: Sample Data Structure 3.3 Blockchain Framework for Health Insurance Claims Integrating blockchain with machine learning combines the strengths of both technologies. Blockchain provides a secure and transparent platform for storing and managing data, while machine learning models can analyze this data to detect fraudulent activities. This integration enhances the reliability and accuracy of fraud detection systems. Recent studies have explored this synergy, demonstrating improved performance in fraud detection tasks [8, 9]. Data for this research is obtained from health insurance claim records, which include both legitimate and fraudulent claims. The preprocessing steps involve datz cleaning, normalization, and feature extraction to ensure data quality and relevance. Techniques such as imputatior are used for handling missing values, and outlier detectior methods are applied.
Table 1: Sample Data Structure 3.3 Blockchain Framework for Health Insurance Claims Integrating blockchain with machine learning combines the strengths of both technologies. Blockchain provides a secure and transparent platform for storing and managing data, while machine learning models can analyze this data to detect fraudulent activities. This integration enhances the reliability and accuracy of fraud detection systems. Recent studies have explored this synergy, demonstrating improved performance in fraud detection tasks [8, 9]. Data for this research is obtained from health insurance claim records, which include both legitimate and fraudulent claims. The preprocessing steps involve datz cleaning, normalization, and feature extraction to ensure data quality and relevance. Techniques such as imputatior are used for handling missing values, and outlier detectior methods are applied.
3.4 Machine Learning Models for Fraud Detection amount, diagnosis code, procedure code, and provider ID.
3.4 Machine Learning Models for Fraud Detection amount, diagnosis code, procedure code, and provider ID.
Fig 1: Blockchain Framework for Health Insurance Claims
Fig 1: Blockchain Framework for Health Insurance Claims
4.2 Machine Learning Algorithms and_ Their Mathematical Foundations The integrated system processes incoming claims through
4.2 Machine Learning Algorithms and_ Their Mathematical Foundations The integrated system processes incoming claims through
Fig 4: Blockchain System Architecture The performance of the integrated system is evaluated using various metrics. For classification tasks, the following metrics are commonly used:
Fig 4: Blockchain System Architecture The performance of the integrated system is evaluated using various metrics. For classification tasks, the following metrics are commonly used:
Fig 6: System Architecture
Fig 6: System Architecture
International Journal of Intelligent Systems and Applications in Engineering
International Journal of Intelligent Systems and Applications in Engineering
Fig 10: Comparison of Fraud Detection Rates and False Positive Rates
Fig 10: Comparison of Fraud Detection Rates and False Positive Rates
Related topics:
Machine LearningAnomaly DetectionPredictive AnalyticsHealthcare TechnologyFraud DetectionCLAIMS MANAGEMENT
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved