An Efficient Heart Disease Prediction System Using Modified Firefly Algorithm Based Radial Basis Function with Support Vector Machine

Q: What is the clinical evidence from this study?

Study design: Observational. Population: Morphological development of cannibalistic Indian Bullfrog tadpoles (n=70). Intervention: Late-stage development (Gosner stages 34-40) vs. Early-stage development (Gosner stages 25-33). Primary outcome: Overall difference in morphometric ratios between early and late developmental stages (p=<0.001).

Q: What does this research mean for the field?

Late-stage cannibalistic Indian Bullfrog tadpoles develop significantly increased body width and oral disc width compared to early-stage tadpoles. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

M Thiyagaraj; Dr G.Suseendran

doi:10.14419/ijet.v7i2.33.17904

June 8, 2018Open Access

An Efficient Heart Disease Prediction System Using Modified Firefly Algorithm Based Radial Basis Function with Support Vector Machine

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Why the study?

Existing heart disease diagnosis systems using Interval Type-2 Fuzzy Logic System and genetic hybrid algorithms have slow training times and fail to achieve high recognition accuracy.

Key Points

Develop an automated heart disease diagnosis system using a modified firefly algorithm and radial basis function support vector machine to overcome the slow training times and lower accuracy of existing fuzzy logic systems.
Structured an input dataset encompassing input, key, and prediction attributes.
Performed data normalization, firefly algorithm-based attribute reduction, and feature extraction using principal component analysis.
Trained a radial basis function support vector machine to classify extracted features as normal or indicative of heart disease.
Mitigated the prolonged training times and modest recognition accuracy associated with interval type-2 fuzzy logic systems and genetic hybrid algorithms.
Achieved automated binary classification distinguishing normal patient profiles from heart disease.

Study Design

Type

Observational (n=70)

Multicenter

No

Structured PICO

P

Population

70 cannibalistic Indian Bullfrog (Hoplobatrachus tigerinus) tadpoles collected from a temporary pond in Sangavi, Pune, Maharashtra, India (39 early-stage [stages 25-33] and 31 late-stage [stages 34-40]). Note: The abstract describes a completely different study using a heart disease dataset.

I

Intervention

Modified firefly algorithm and Radial Basis Function based Support Vector Machine (MFA and RBF-SVM) for heart disease prediction (Abstract). Morphometric analysis of tadpole development (Full text).

C

Comparator

Interval Type-2 Fuzzy Logic System (IT2FLS) and genetic hybrid algorithms (Abstract). Early-stage vs. late-stage tadpoles (Full text).

O

Outcome

Recognition accuracy for heart disease (Abstract). Morphometric ratios including BW/TOL and OD/BW (Full text).surrogate

Main Result

p-value: p=<0.001

Late-stage cannibalistic tadpoles develop wider oral cavities and increased body width, reflecting morphological trade-offs for capturing large prey while avoiding predation.

Implication

May support faster heart disease diagnosis; leaves open real-world utility of modified firefly-SVM pending prospective validation.

Abstract

Nowadays Heart Disease is one among the main roots of death in and around countries. Accurately predicting the heart disease is difficult for doctors. Thus, it is obligatory to apply computerized technologies to support doctors for diagnose faster with greater accuracy. An existing work introduced a heart disease diagnosis system which is dependent upon Interval Type-2 Fuzzy Logic System (IT2FLS). However, the training time of IT2FLS as well as genetic hybrid algorithms is quite gentle. And also it does not achieve high recognition accuracy. To solve this problem the proposed system introduced a modified firefly algorithm and Radial Basis Function based Support Vector Machine (MFA and RBF-SVM). An input dataset encompasses 3 kinds of attributes such as Input, Key and Prediction attributes. After the normalization, an attribute reduction and feature extraction are performed by using FA and Principal Component Analysis (PCA) respectively. Finally RBF-SVM is classified a features as normal or heart diseases.

Discussion

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Cite This Study

Thiyagaraj et al. (2018) conducted an observational in Morphological development of cannibalistic Indian Bullfrog tadpoles (n=70). Late-stage development (Gosner stages 34-40) vs. Early-stage development (Gosner stages 25-33) was evaluated on Overall difference in morphometric ratios between early and late developmental stages (p=<0.001).

synapsesocial.com/papers/69f6e5ac8071d4f1bdfc64db https://doi.org/10.14419/ijet.v7i2.33.17904

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An Efficient Heart Disease Prediction System Using Modified Firefly Algorithm Based Radial Basis Function with Support Vector Machine

Key Points

Study Design

Structured PICO

Main Result

Implication

Abstract

Discussion

Cite This Study

An Efficient Heart Disease Prediction System Using Modified Firefly Algorithm Based Radial Basis Function with Support Vector Machine

Key Points

Study Design

Structured PICO

Main Result

Implication

Abstract

Discussion

Cite This Study

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