Machine-Learning-Based Diabetes Mellitus Risk Prediction Using Multi-Layer Neural Network No-Prop Algorithm

J, Jeba Sonia; Jayachandran, Prassanna; Quadir, Abdul; Sivaraman, Arun Kumar; Tee, Kong Fah

doi:10.3390/diagnostics13040723

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…A multi-layer neural network is used to improve the efficiency of categorization. The best specificity and sensitivity values of 0.95% were achieved by the suggested multi-layer neural network [28].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Diabetes Prediction: An Improved Boosting Algorithm for Diabetes Prediction

Alam,

Ferdous,

Neera

2024

ijacsa

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Diabetes is increasing gradually due to the inability to effectively use the human body's insulin, which threatens public health. People with diabetes who go undiagnosed at early stages or who have diabetes have a high risk of heart disease, kidney disease, eye problems, stroke, and nerve damage for which diabetes diagnosis is crucial to prevent. Our advanced machine learning algorithm is the gateway to a revolutionary possibility of detecting whether the human body has diabetes. Developed this method based on machine learning with one lakh data and the main objective of creating a new and novel diabetes prediction model named moderated Ada-Boost(AB) that can accurately diagnose diabetes. About 10 different classification methods are applied in this research such as Random forest classifier (RF), logistic regression (LR), decision tree classifier (DT), support vector machine (SVM), Bayesian Classifier (BC) or Naive Bayes Classifier (NB), Bagging Classifier (BG), Stacking Classifier (ST), Moderated Ada-Boost(AB) Classifier, K Neighbors Classifier (KN) and Artificial Neural Network (ANN). The crucial contribution is to find out the appropriate values for the different models using the hyper-parameter tuning process. We have proposed a new boosting model named Moderated Ada-Boost(AB) which is the combination of the hyper-parameter tuned random forest model and Ada-boost model. Different evaluation metrics such as accuracy, precision, recall, f1 score, and others are used to evaluate the performance of the models. Our proposed new boosting algorithm named Moderated Ada-Boost(AB) provides better accuracy than other models whose training accuracy is 99.95% and testing accuracy is 98.14%.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Diabetes Prediction: An Improved Boosting Algorithm for Diabetes Prediction

Alam,

Ferdous,

Neera

2024

ijacsa

View full text Add to dashboard Cite

show abstract

“…Researchers [ 23 ] have implemented an actual heuristic for its solution. It integrated probabilistic and deterministic variable fixing schemes.…”

Section: Literature Workmentioning

confidence: 99%

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection

Nithiyanantham¹,

Sivaraman²

2023

Biomedicines

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Wireless Body Area Network (WBAN) is a trending technology of Wireless Sensor Networks (WSN) to enhance the healthcare system. This system is developed to monitor individuals by observing their physical signals to offer physical activity status as a wearable low-cost system that is considered an unremarkable solution for continuous monitoring of cardiovascular health. Various studies have discussed the uses of WBAN in Personal Health Monitoring systems (PHM) based on real-world health monitoring models. The major goal of WBAN is to offer early and fast analysis of the individuals but it is not able to attain its potential by utilizing conventional expert systems and data mining. Multiple kinds of research are performed in WBAN based on routing, security, energy efficiency, etc. This paper suggests a new heart disease prediction under WBAN. Initially, the standard patient data regarding heart diseases are gathered from benchmark datasets using WBAN. Then, the channel selections for data transmission are carried out through the Improved Dingo Optimizer (IDOX) algorithm using a multi-objective function. Through the selected channel, the data are transmitted for the deep feature extraction process using One Dimensional-Convolutional Neural Networks (ID-CNN) and Autoencoder. Then, the optimal feature selections are done through the IDOX algorithm for getting more suitable features. Finally, the IDOX-based heart disease prediction is done by Modified Bidirectional Long Short-Term Memory (M-BiLSTM), where the hyperparameters of BiLSTM are tuned using the IDOX algorithm. Thus, the empirical outcomes of the given offered method show that it accurately categorizes a patient’s health status founded on abnormal vital signs that is useful for providing the proper medical care to the patients.

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“…In data preprocessing, feature scaling with a standard scaler is performed. The standard scaler is one of the most commonly employed types of feature scaling methods for distance-based learners such as KNN, and SVM [41], [42], [43], [44]. Standard scaler improves the performance of distance based learning algorithm [45], [46], [47], [48], [49].…”

Section: B Data Preprocessingmentioning

confidence: 99%

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

Assegie,

Suresh,

Purushothaman

et al. 2023

Journal of Robotics and Control

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Diabetes is one of the quickly spreading chronic diseases causing health complications, such as diabetes retinopathy, kidney failure, and cardiovascular disease. Recently, machine-learning techniques have been widely applied to develop a model for the early prediction of diabetes. Due to its simplicity and generalization capability, K-nearest neighbor (KNN) has been one of the widely employed machine learning techniques for diabetes prediction. Early diabetes prediction has a significant role in managing and preventing complications associated with diabetes, such as retinopathy, kidney failure, and cardiovascular disease. However, the prediction of diabetes in the early stage has remained challenging due to the accuracy and reliability of the KNN model. Thus, gird search hyperparameter optimization is employed to tune the K values of the KNN model to improve its effectiveness in predicting diabetes. The developed hyperparameter-tuned KNN model was tested on the diabetes dataset collected from the UCI machine learning data repository. The dataset contains 768 instances and 8 features. The study applied Min-max scaling to scale the data before fitting it to the KNN model. The result revealed KNN model performance improves when the hyperparameter is tuned. With hyperparameter tuning, the accuracy of KNN improves by 5.29% accuracy achieving 82.5% overall accuracy for predicting diabetes in the early stage. Therefore, the developed KNN model applied to clinical decision-making in predicting diabetes at an early stage. The early identification of diabetes could aid in early intervention, personalized treatment plans, or reducing healthcare costs reducing associated risks such as retinopathy, kidney disease, and cardiovascular disease.

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Machine-Learning-Based Diabetes Mellitus Risk Prediction Using Multi-Layer Neural Network No-Prop Algorithm

Cited by 13 publications

References 17 publications

Enhancing Diabetes Prediction: An Improved Boosting Algorithm for Diabetes Prediction

Enhancing Diabetes Prediction: An Improved Boosting Algorithm for Diabetes Prediction

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection

Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier

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