Natural Intelligence Based Automatic Knowledge Discovery for Medical Practitioners

Mangat, Veenu

doi:10.1007/978-1-4614-0373-9_5

Cited by 3 publications

(5 citation statements)

References 21 publications

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“…In a pilot study conducted in (Mangat, 2011), these methods have shown reasonably good accuracies in the range of 91-94% while maintaining the comprehensibility of the rules as measured using size of rule and rule sets. To tailor the system to medical domain, a new fitness function can be embedded into the process.…”

Section: Proposed Method: Hosementioning

confidence: 99%

“…The rule discovery, fitness evaluation and clipping of individual terms is the same as for ACO/PSO with PF (Mangat, 2011).…”

Section: Proposed Method: Hosementioning

confidence: 99%

“…where, MinTP is the lower threshold for number of correctly covered examples by a rule (Mangat, 2011). The second modification is to compute quality of not just the individual rules in rule set, but also compute and evaluate the complete rule set quality.…”

Section: Proposed Method: Hosementioning

confidence: 99%

“…Four techniques based on swarm intelligence were studied and implemented in (Mangat, 2011) and they provided good accuracy values when compared to other traditional non SI based mining techniques. A new quality criterion combined with Shuffled frog leaping technique, showed very good results.…”

Section: Jcsmentioning

confidence: 99%

“…Similar parameter settings have been used as in hybrid ACO/PSO with PF (Mangat, 2011). For the ACO component, parameter values were set as given: Number of Ants = 500, Minimum number of records a rule covers = 10, maximum uncovered examples of each class = 15 and number of rules to test if ant has converged = 20.…”

Section: Parameter Settingsmentioning

confidence: 99%

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An Algorithm for Mining Usable Rules Using a Holistic Swarm Based Approach

Mangat

Vig

2014

Journal of Computer Science

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Evolutionary algorithms are capable of finding near optimal solutions to problems which are intractable to solve using conventional methods. One such problem is to accurately classify patients using rule mining methodology while controlling the size of output rules. A massive amount of data pertaining to medicine is generated and recorded daily. Uncovering useful knowledge and assisting decision makers in the diagnosis and treatment of diseases from this vast data has become imperative. Association rule mining is an obvious choice for representing this previously hidden information as rules are simple to understand and infer. These rules can be used to understand the etiology of diseases and classify patients based on recorded characteristics. The interestingness of such an algorithm for rule mining will be determined by its accuracy and ability to produce easily understandable rules. This study applies latest improvements in swarm intelligence to devise a novel strategy for rule mining that exhibits high predictive accuracy and comprehensibility. It has been applied over four medical datasets to classify patients as fit or unfit. The paper begins with an explanation of rule mining functionality and concept of swarm intelligence. The current techniques for rule mining in the medical domain are surveyed and their shortcomings are identified. This is followed by a description of the proposed algorithm which includes a novel rule discovery procedure and a novel rule list selection criterion. The results of the proposed algorithm thus obtained, are compared with the other best known approaches. Finally, the future scope of work in this area is briefly discussed.

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Section: Proposed Method: Hosementioning

confidence: 99%

“…The rule discovery, fitness evaluation and clipping of individual terms is the same as for ACO/PSO with PF (Mangat, 2011).…”

Section: Proposed Method: Hosementioning

confidence: 99%

Section: Proposed Method: Hosementioning

confidence: 99%

Section: Jcsmentioning

confidence: 99%

Section: Parameter Settingsmentioning

confidence: 99%