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).

May 3, 2026Open Access

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

Q: What are the key findings of this study?

Late-stage cannibalistic Indian Bullfrog tadpoles developed significantly increased body width and oral disc width compared to early-stage tadpoles.

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.

Q: What question did this study set out to answer?

The study aims to develop a more accurate heart disease prediction system using modified firefly algorithm and radial basis function.

Key Result

Late-stage cannibalistic Indian Bullfrog tadpoles developed significantly increased body width and oral disc width compared to early-stage tadpoles.

Key Points

The study aims to develop a more accurate heart disease prediction system using modified firefly algorithm and radial basis function.
Utilized a modified firefly algorithm and radial basis function-based support vector machine (MFA and RBF-SVM) for prediction.
Normalized input datasets and performed attribute reduction and feature extraction using feature analysis (FA) and principal component analysis (PCA).
Classified features as either normal or indicative of heart disease using the developed system.
Achieved significant improvement in prediction accuracy compared to traditional methods, although specific metrics were not disclosed.
Demonstrated faster diagnosis times through computational techniques, enhancing support for doctors.
Proposed system effectively differentiates between normal and heart disease attributes based on the processed data.

Study Design

Type

Observational (n=70)

Multicenter

Structured PICO

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.

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).

Comparator

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

Outcome

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

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

Main Result

p-value: p=<0.001

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.

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