A Study on Various Machine Learning Algorithms Used for Prediction of Diabetes Mellitus

Pradhan, Gaurav N.; Pradhan, Ratika; Khandelwal, Bidita

doi:10.1007/978-981-15-7394-1_50

Cited by 14 publications

(4 citation statements)

References 14 publications

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“…In [4] "An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier"…”

Section: Related Workmentioning

confidence: 99%

Prediction of Diabetes Using Machine Learning Algorithms

K.V.

2024

IJSREM

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In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. These models analyze the dataset to determine whether a diabetes diagnosis is positive or negative. The dataset used in this research is divided into training data and testing data with a ratio of 70:30 respectively. These three models gave us different accuracies and simple to use. A cloud storage system stores the fused models for future use. Based on the patient’s real-time medical record, the fused model predicts whether the patient is diabetic or not. The proposed fused ML model has a prediction accuracy, which is higher than the previously published methods. Keywords: Logistic Regression, SVM, ANN, ML techniques

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“…In [4] "An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier"…”

Section: Related Workmentioning

confidence: 99%

Prediction of Diabetes Using Machine Learning Algorithms

K.V.

2024

IJSREM

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“…Pradhan et al research [16] employed supervised learning, which involves training models on labelled data to make predictions, to develop models for diabetes diagnosis. Additionally, they utilized hybrid learning, which combines multiple learning techniques, to further enhance the performance of the diagnostic models.…”

Section: Kavakiotis Et Al's Papermentioning

confidence: 99%

Diabetes Prediction Empowered with Multi-level Data Fusion and Machine Learning

Bassam,

Rouai,

Ahmad

et al. 2023

IJACSA

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Technology improvements have benefited the medical industry, especially in the area of diabetes prediction. In order to find patterns and risk factors related to diabetes, machine learning and Artificial Intelligence (AI) are vital in the analysis of enormous volumes of data, including medical records, lifestyle variables, and biomarkers. This makes it possible for tailored management and early discovery, which might revolutionize healthcare. This study examines how machine learning algorithms may be used to identify diseases, with an emphasis on diabetes prediction. The Proposed Diabetes Prediction Empowered with Mutli-level Data Fusion and Machine Learning (DPEMDFML) model combines two distinct types of models-the Artificial Neural Network (ANN) and the Support Vector Machine (SVM)-to create a fused machine learning technique. Two separate datasets were utilized for training and testing the model in order to assess its performance. To ensure a thorough evaluation of the model's prediction ability, the datasets were split in two experiments in proportions of 70:30 and 75:25, respectively. The study's findings were encouraging, with the ANN algorithm obtaining a remarkable accuracy of 97.43%. This indicates that the model accurately identified instances of diabetes, indicating a high degree of accuracy. A more thorough knowledge of the model's prediction ability would result from further assessment and validation of its performance using various measures.

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“…Recent literature has produced a significant amount of research to recognize diabetic patients based on symptoms by applying machine-learning techniques. Based on supervised learning, hybrid learning, or ensemble learning, G. Pradhan et al [4] applied various algorithms for diagnosing diabetes mellitus to gain higher accuracy rate, but the ensemble approach performs better than the other two approaches. In an ensemble approach, S.Kumari et al [5] improved classification accuracy by applying a soft voting classifier to the Pima-Diabetes dataset and Breast-Cancer dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Diabetes Empowered With Fused Machine Learning

et al. 2022

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In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understand its symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. This article presents a model using a fused machine learning approach for diabetes prediction. The conceptual framework consists of two types of models: Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. These models analyze the dataset to determine whether a diabetes diagnosis is positive or negative. The dataset used in this research is divided into training data and testing data with a ratio of 70:30 respectively. The output of these models becomes the input membership function for the fuzzy model, whereas the fuzzy logic finally determines whether a diabetes diagnosis is positive or negative. A cloud storage system stores the fused models for future use. Based on the patient's real-time medical record, the fused model predicts whether the patient is diabetic or not. The proposed fused ML model has a prediction accuracy of 94.87, which is higher than the previously published methods.

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A Study on Various Machine Learning Algorithms Used for Prediction of Diabetes Mellitus

Cited by 14 publications

References 14 publications

Prediction of Diabetes Using Machine Learning Algorithms

Prediction of Diabetes Using Machine Learning Algorithms

Diabetes Prediction Empowered with Multi-level Data Fusion and Machine Learning

Prediction of Diabetes Empowered With Fused Machine Learning

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