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This research aims to evaluate the performance of the Multi-Layer Perceptron model for diabetes detection.

Diabetes diagnosis using Multi-Layer Perceptron model

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This research aims to evaluate the performance of the Multi-Layer Perceptron model for diabetes detection.
Evaluated performance of Multi-Layer Perceptron with varying hidden neurons from 50 to 100.
Used the Pima Indian Diabetes Dataset for training and testing the model.
Different performance metrics were utilized to analyze MLP effectiveness.
Achieved highest performance at 90 hidden neurons with an accuracy of 85.345%.
Provided insights into how network configurations impact MLP performance for diabetes detection.

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Research demonstrates improved diabetes diagnosis using Multi-Layer Perceptron by optimizing network performance metrics.

A 2025 study studied this question.

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