Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques

Mushtaq, Zaigham; Ramzan, Muhammad Farhan; Ali, Sikandar; Baseer, Samad; Samad, Ali; Husnain, Mujtaba

doi:10.1155/2022/6521532

Cited by 36 publications

(25 citation statements)

References 46 publications

(64 reference statements)

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“…During the research period the algorithms NB, LR, and RF were applied. Random Forest was depicts to have the best accuracy on their test database after being evaluated using Tenfold Cross Validation and Percentage Split [10,[18][19][20][21][22]. In the end, they have suggested a simple method for the user to determine diabetes by assessing their characteristics.…”

Section: Literature Reviewmentioning

confidence: 99%

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Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora

Bhat

Selvam

Ansari

et al. 2022

Computational Intelligence and Neuroscience

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Diabetes is one of the biggest health problems that affect millions of people across the world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage, blindness, and other illnesses. Researchers are motivated to create a Machine Learning methodology that can predict diabetes in the future. Exploiting Machine Learning Algorithms (MLA) is essential if healthcare professionals are able to identify diseases more effectively. In order to improve the medical diagnosis of diabetes this research explored and contrasts various MLA that can identify diabetes risk early. The research includes the analysis on real datasets such as a clinical dataset gathered from a doctor in the Indian district of Bandipora in the years April 2021–Feb2022. MLA are currently important in the healthcare sector due to their prediction abilities. Researchers are using MLA to improve disease prediction and reduce cost. In this Paper author developed a methodology using Machine Learning Algorithms for Diabetes Disease Risk Prediction in North Kashmir. Six MLA have been successfully used in the experimental study such as Random Forest (RF), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boost (GB), Decision Tree (DT), and Logistic Regression (LR). RF is the most accurate classifier with the uppermost accuracy rate of 98 percent followed by MLP (90.99%), SVM (92%), GBC (97%), DT (96%), and LR (69%), respectively, with the balanced data set. Lastly, this study enables us to effectively identify the prevalence and prediction of diabetes.

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Section: Literature Reviewmentioning

confidence: 99%

“…e main objective is to forecast Prediabetes using AI and ML [10,[14][15][16][17][18][19][20][21][22][23][24][25][26][27]. In this research work, the author used wellknown MLA approaches to examine actual diagnostic medical data based on various risk factors to assess their effectiveness for diabetic probability.…”

Section: Literature Reviewmentioning

confidence: 99%

Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora

Bhat

Selvam

Ansari

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…e parameters of RBM used are as follows: a restricted Boltzmann machine with binary visible units and binary hidden units. Parameters are estimated using stochastic maximum likelihood (SML), also known as persistent contrastive divergence (PCD) [36]. e number of hidden units has set to its default value that is 256, whereas the learning rate value is 0.1. e batch size has been set to 10, whereas the number of iterations has been varied from 10 to 50 with and increment of 5 to evaluate the performance for different iterations.…”

Section: Precisionmentioning

confidence: 99%

Pneumonia Detection in Chest X-Ray Images Using Enhanced Restricted Boltzmann Machine

Wahid

Azhar

Ali

et al. 2022

Journal of Healthcare Engineering

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The process of pneumonia detection has been the focus of researchers as it has proved itself to be one of the most dangerous and life-threatening disorders. In recent years, many machine learning and deep learning algorithms have been applied in an attempt to automate this process but none of them has been successful significantly to achieve the highest possible accuracy. In a similar attempt, we propose an enhanced approach of a deep learning model called restricted Boltzmann machine (RBM) which is named enhanced RBM (ERBM). One of the major drawbacks associated with the standard format of RBM is its random weight initialization which leads to improper feature learning of the model during the training phase, resulting in poor performance of the machine. This problem has been tried to eliminate in this work by finding the differences between the means of a specific feature vector and the means of all features given as inputs to the machine. By performing this process, the reconstruction of the actual features is increased which ultimately reduces the error generated during the training phase of the model. The developed model has been applied to three different datasets of pneumonia diseases and the results have been compared with other state of the art techniques using different performance evaluation parameters. The proposed model gave highest accuracy of 98.56% followed by standard RBM, SVM, KNN, and decision tree which gave accuracies of 97.53%, 92.62%, 91.64%, and 88.77%, respectively, for dataset named dataset 2. Similarly, for the dataset 1, the highest accuracy of 96.66 has been observed for the eRBM followed by srRBM, KNN, decision tree, and SVM which gave accuracies of 90.22%, 89.34%, 87.65%, and 86.55%, respectively. In the same way, the accuracies observed for the dataset 3 by eRBM, standard RBM, KNN, decision tree, and SVM are 92.45%, 90.98%, 87.54%, 85.49%, and 84.54%, respectively. Similar observations can also be seen for other performance parameters showing the efficiency of the proposed model. As revealed in the results obtained, a significant improvement has been observed in the working of the RBM by introducing a new method of weight initialization during the training phase. The results show that the improved model outperforms other models in terms of different performance evaluation parameters, namely, accuracy, sensitivity, specificity, F1-score, and ROC curve.

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“…Therefore automatic diabetes detection based on a combination of factors can assist clinicians in treating patients more effectively and efficiently. Machine learning based solutions are always promising because they learn from actual features to diagnose diabetes (5) . The enormous amount of data generated in this field presents two significant challenges for researchers and developers trying to construct diabetes predictive models.…”

Section: Introductionmentioning

confidence: 99%

“…Related Work (5) used the KNN and Naive Bayes approach to predict diabetes. Their method is implemented as professional software, where users enter input in the form of patient data to determine if a patient has diabetes.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning Approach for Effective Prediction of Diabetes Mellitus Using Hard Voting Classifier

Atif¹,

Anwer²,

Talib³

2022

IJST

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Objectives: People all across the world are afflicted by the deadly ailment known as diabetes. Diabetes is a terrible condition characterized by high blood glucose levels. This chronic condition is one of the leading causes of death for people worldwide. Early identification and prediction of diabetes can be aided by machine learning techniques. The purpose of this study is to use an ensemble of machine learning algorithms to predict diabetes efficiently in order to help the patients suffering from this lethal disease. Methods: The existing methods use a single model to predict diabetes, which may have an impact on accuracy because no one model can fit all datasets. Therefore we propose a robust model based on ensemble learning using hard voting classifier. Both the Pima Indians Diabetes dataset and the Early Stage Diabetes Risk Prediction Dataset, which collect data on people with and without diabetes, were tested. For classification, the proposed ensemble hard voting classifier uses a combination of three machine learning algorithms namely logistic regression, decision tree, and support vector machine. Findings: On the PIMA diabetes dataset, the proposed ensemble approach achieves the highest accuracy, precision, recall, and F1 score value of 81.17%, while on the Early Stage Diabetes Risk Prediction Dataset, it achieves the highest accuracy, precision, recall, and F1 score value of 94.23%. Novelty: The proposed methodology was experimentally tested using the state-of-the-art technology and basic classifiers such as K-Nearest Neighbor, Logistic Regression, Support Vector Machine, and Random Forest. The results are validated by computing the confusion matrix and ROC for each classier type.

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Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques

Cited by 36 publications

References 46 publications

Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora

Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora

Pneumonia Detection in Chest X-Ray Images Using Enhanced Restricted Boltzmann Machine

An Ensemble Learning Approach for Effective Prediction of Diabetes Mellitus Using Hard Voting Classifier

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