A highly accurate firefly based algorithm for heart disease prediction

Long, Nguyen Cong; Meesad, Phayung; Unger, Herwig

doi:10.1016/j.eswa.2015.06.024

Cited by 194 publications

(87 citation statements)

References 24 publications

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“…The firefly algorithm can also participated in (42-44) (45), the fireflies also participated (46)(47)(48). firefly algorithm also involved for (49,50).…”

Section: B Applications Areas Of Fire Fly Optimizationmentioning

confidence: 99%

A Review of Bio Inspired Computing and its Applications

kumar¹,

Bhargava²

2018

J.JBTW

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Bio Inspired computation is the part of Artificial intelligence which was inspired by the biological behaviors of biological systems. Swarm intelligence is the collective behavior of an organized group in day-to-day life. Common examples of swarm intelligence include ant colony, bee colony, etc. and some are non-swarm intelligence like bat algorithm, etc. This study mainly focuses on application areas of various bio inspired computing based swarm and non-swarm intelligence. This review also discusses the newly developed algorithms. Specific application areas of such algorithms have been discussed in this research. This research highlighted the future scope of present algorithms.

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“…The firefly algorithm can also participated in (42-44) (45), the fireflies also participated (46)(47)(48). firefly algorithm also involved for (49,50).…”

Section: B Applications Areas Of Fire Fly Optimizationmentioning

confidence: 99%

A Review of Bio Inspired Computing and its Applications

kumar¹,

Bhargava²

2018

J.JBTW

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“…On the other hand, the firefly-levy flight algorithm is used for the continuous problems. To handle this, a tanh function is used which is defines as [1]:…”

Section: Fig1 Flow Chart Of Proposed Approach For Medical Diagnosismentioning

confidence: 99%

“…The fitness function used in this research is as [1]: Binary coding: The 0 and 1 encoding scheme is used in the attribute reduction. It shows that this is a discrete problem.…”

Section: Fig1 Flow Chart Of Proposed Approach For Medical Diagnosismentioning

confidence: 99%

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A Hybrid Nature-Inspired Classification Technique for Medical Diagnosis

Muwal¹,

Kumar²

2016

IJCA

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Early detection of heart disease is essential in medical system because heart disease is the major cause of decease both for men and women. For the medical diagnosis, numerous soft computing techniques are available like Ant Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, Firefly Algorithm, Cuckoo Search, Levy Flight etc. The combination of all these evolutionary techniques with the other techniques like artificial neural network, rough set, fuzzy logic and etc. are also possible. The proposed algorithm uses a rough set based attribute reduction with firefly-levy algorithm and the fuzzy logic system for heart disease detection. The combination of these techniques is used to handle the dataset with high dimension and uncertainties. The attribute reduction method is used with the firefly-levy flight algorithm. This will reduce the burden and enhance the performance of classifier. The experiment results show a considerable supremacy of proposed algorithm when compared with other artificial intelligence techniques.

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“…In some of the recent research works [2,11,12,14] the importance is given to single algorithm only. The dependence of heart disease prediction relies solely on a single algorithm turns the decision making system prone to error.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Classification Algorithms on a Novel Unified Clinical Decision Support Model for Predicting Coronary Heart Disease Risks

Puyalnithi¹,

Viswanatham²

2017

IJIES

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Abstract:Most of the existing Clinical Decision Support Systems(CDSS) for predicting Coronary Heart Disease(CHD) risks rely solely on the analysis of single type of diagnostic data or a certain type of classification algorithm, thus making the predicted component unreliable. Since the Risk value very much influences the further course of the treatment, the correctness of the risk value is eminent. So the novelty of the proposed method lies in considering many classification algorithms and many diagnostics data sets to predict the risk value. Moreover in the proposed method we have given flowchart for finding the risk value, which is a novelty. The results shows that the selection of classification algorithms cannot be universal as proposed by many recent researches and each type of data set will be having a certain type of classifier which works best for the data set. The proposed system has four classifiers namely Mixed cardio data classifier, Single Photon Emission Computed Tomography(SPECT) data classifier, Electrocardiogram(ECG) data classifier and 1-year survival Electrocardiogram data classifier, each trained with benchmark datasets of University of California, Irvine(UCI) Machine learning repository. The performance of algorithms such as Iterative Dichotomiser3(ID3), Naïve Bayes(NB), Support Vector Machine(SVM), k-Nearest Neighbors(kNN) and Ensemble classifiers such as Bagging and Boosting are evaluated for every data set and best classifier is chosen by its accuracy measure. Ensemble method is the most efficient for 1-year survival ECG data classifier with 89.5% accuracy, ID3 is the most efficient algorithm for Mixed cardiac data classifier with 96.9% accuracy and SVM is the most efficient algorithm for SPECT data classifier and ECG data Classifier with accuracy of 98.3% and 100% respectively. The proposed work also finds the optimal number of samples needed for Bagging and Boosting type of ensemble classifiers.

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A highly accurate firefly based algorithm for heart disease prediction

Cited by 194 publications

References 24 publications

A Review of Bio Inspired Computing and its Applications

A Review of Bio Inspired Computing and its Applications

A Hybrid Nature-Inspired Classification Technique for Medical Diagnosis

Performance Analysis of Classification Algorithms on a Novel Unified Clinical Decision Support Model for Predicting Coronary Heart Disease Risks

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