Improving diagnostic accuracy using agent-based distributed data mining system

Abstract

The use of data mining techniques to improve the diagnostic system accuracy is investigated in this paper. The data mining algorithms aim to discover patterns and extract useful knowledge from facts recorded in databases. Generally, the expert systems are constructed for automating diagnostic procedures. The learning component uses the data mining algorithms to extract the expert system rules from the database automatically. Learning algorithms can assist the clinicians in extracting knowledge automatically. As the number and variety of data sources is dramatically increasing, another way to acquire knowledge from databases is to apply various data mining algorithms that extract knowledge from data. As data sets are inherently distributed, the distributed system uses agents to transport the trained classifiers and uses meta learning to combine the knowledge. Commonsense reasoning is also used in association with distributed data mining to obtain better results. Combining human expert knowledge and data mining knowledge improves the performance of the diagnostic system. This work suggests a framework of combining the human knowledge and knowledge gained by better data mining algorithms on a renal and gallstone data set.

Research on wireless distributed financial risk data stream mining based on dual privacy protection

“…However, when designing a privacy protection algorithm, it is impossible to accurately predict the amount of a priori knowledge that an attacker can grasp, so no strong proof can be given for an unknowable attack. Differential privacy provides a strong, provable and strict mathematical guarantee for privacy protection algorithm protection [23].…”

Research on Distributed Data Stream Mining of Financial Risk Based on Double Privacy Protection

“…designing a privacy protection algorithm, it is impossible to accurately predict the amount of a priori knowledge that an attacker can grasp, so no strong proof can be given for an unknowable attack. Differential privacy provides a strong, provable and strict mathematical guarantee for privacy protection algorithm protection [23].…”

Research on wireless distributed financial risk data stream mining based on dual privacy protection

“…However, when designing a privacy protection algorithm, it is impossible to accurately predict the amount of a priori knowledge that an attacker can grasp, so no strong proof can be given for an unknowable attack. Differential privacy provides a strong, provable and strict mathematical guarantee for privacy protection algorithm protection [23]. Region M multiple nodes publish privacy protection data in real time, and integrate data from each node for data mining [24].…”