Key result
The proposed ANFIS model achieved an accuracy of 95.5% in predicting diabetes, outperforming traditional machine learning algorithms such as SVM, Naïve Bayes, and KNN.
Why the study?
The study was conducted to develop an IoT- and machine learning-based framework using an Adaptive Neuro-Fuzzy Inference System to predict diabetes and enable real-time patient monitoring.
Does an Adaptive Neuro-Fuzzy Inference System (ANFIS) improve classification accuracy for diabetes prediction in pregnant women?
Does an Adaptive Neuro-Fuzzy Inference System (ANFIS) improve classification accuracy for diabetes prediction in pregnant women?
Absolute Event Rate: 95.5% vs 94%
An IoT-based ANFIS machine learning framework achieved 95.5% accuracy in predicting diabetes, demonstrating superior performance over conventional algorithms like SVM and KNN.
May aid real-time diabetes monitoring; leaves open prospective validation before clinical use.
The increasing integration of the Internet of Things (IoT) in healthcare is revolutionizing patient monitoring and disease prediction. This paper presents a machine learning (ML)-based framework using Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict diabetes. The proposed system leverages IoT data to monitor key health parameters, including glucose levels, blood pressure, and age, offering real-time diagnostics for diabetes patients. The dataset used in this study was obtained from the UCI repository and underwent preprocessing, feature selection, and classification using the ANFIS model. Comparative analysis with other machine learning algorithms, such as Support Vector Machines (SVM), Naïve Bayes, and K-Nearest Neighbors (KNN), demonstrates that the proposed method achieves superior predictive performance. The experimental results show that the ANFIS model achieved an accuracy of 95.5%, outperforming conventional models, and providing more reliable decision-making in clinical settings. This study highlights the potential of combining IoT with machine learning to improve predictive healthcare applications, emphasizing the need for real-time patient monitoring systems.
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Sunny et al. (2024) studied Diabetes. Adaptive Neuro-Fuzzy Inference System (ANFIS) framework vs. Traditional machine learning algorithms (SVM, Naïve Bayes, KNN) was evaluated on Classification accuracy for diabetes prediction. The proposed ANFIS model achieved an accuracy of 95.5% in predicting diabetes, outperforming traditional machine learning algorithms such as SVM, Naïve Bayes, and KNN.
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