Machine Learning and Data Mining in Diabetes Diagnosis and Treatment

He, Bo; Shu, Kuang-I; Zhang, Heng

doi:10.1088/1757-899x/490/4/042049

Cited by 13 publications

(4 citation statements)

References 13 publications

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“…e autonomous variable response affects a lot on the target/dependent variable, as shown in equation ( 4). We use a simplified hypothesis and cost function for multivariate linear regression, as there are eight different variables in our dataset [57]. We choose a very simplified hypothesis function (h θ (x)).…”

Section: Linear Regressionmentioning

confidence: 99%

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Butt

Letchmunan

Ali

et al. 2021

Journal of Healthcare Engineering

117

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The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.

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Section: Linear Regressionmentioning

confidence: 99%

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Butt

Letchmunan

Ali

et al. 2021

Journal of Healthcare Engineering

117

View full text Add to dashboard Cite

show abstract

“…Hence an organized way of collecting these data is necessary. To manage their patients' data efficiently and methodically, hospitals are now embracing an information management system (IMS) strategy [3,4]. Such systems generate a large amount of data that is expressed by graphs, statistics, text, and images.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection with PSO and Convolutional Neural Network with Long Short-Term Memory for Medical Application

ASWATHI,

KUMAR,

RAMAKRISHNAN

2024

Preprint

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The presence of cardiovascular disease is the main cause of fatalities and illnesses throughout. Predicting heart disease prognosis in medical data analytics is quite difficult. The spectacular amount of unstructured data produced by search has demonstrated that significant features are crucial in enhancing the effectiveness of machine learning models. In this study, the dataset of hospitalised patients' heart failure survivors is analysed. It is important to discover critical characteristics and effective deep-learning methodologies to increase the accuracy of survival prediction for cardiovascular patients. This research is approached by using K-Means segmentation for partitioning data. After segmentation, the highest-ranking features chosen by Particle Swarm Optimization (PSO) are used to train machine learning models. Finally, trained data of patients are classified using a hybrid technique combining Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). The classification attains an overall accuracy of 94.63%, thereby supporting experts to predict disease earlier. The examination of the proposed is accomplished through Python Software and the outcomes reveal that the hybrid classification technique performs better metrics when contrasted with state-of-art approaches.

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“…Data mining techniques have been extensively used in medicine to develop models that enhance the prediction accuracy of heart diseases, 19–22 diabetic patients, 23–25 lung cancer, 26,27 and hematological abnormalities. 28…”

Section: Introductionmentioning

confidence: 99%

“…17 Data mining techniques will reduce human errors and help to improve the identification of key risk factors, and to prompt the patients' treatment. 18 Data mining techniques have been extensively used in medicine to develop models that enhance the prediction accuracy of heart diseases, [19][20][21][22] diabetic patients, [23][24][25] lung cancer, 26,27 and hematological abnormalities. 28 Besides, prediction models with data mining techniques including regression and partitioning algorithms, 29 Naïve Bayes, Decision Trees, and Neural Networks.…”

Section: Introductionmentioning

confidence: 99%

The association of vitamin-D level with catheter-related-thrombosis in hemodialysis patients: A data mining model

et al. 2021

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Purpose: This study aims to investigate the association of different risk factors including vitamin-D level with catheter-related-thrombosis in hemodialysis patients by applying data mining techniques. Methods: This study used the retrospectively approach and was done based on the CRISP-DM framework. The data of 1048 hemodialysis patients of Hasheminejad Kidney Center whose first catheterization was between 2014 and 2019 was used for analysis. In this study, patients with a previous history of deep venous thrombosis, thrombophilic condition, and undergone anticoagulant therapy were excluded. The decision tree J48 in WEKA software was used for modeling. The K-fold cross-validation method was also used to evaluate the classification performance. Finally, the model was evaluated by using Confusion Matrix and F-Measure. Results: The approved model was accurate in 70.3% of the predictions, and it demonstrated an association between patients’ vitamin-D range and catheter-related-thrombosis. Conclusion: The results indicated that in hemodialysis patients without the history of cancer or high blood pressure, vitamin-D had a significant impact on catheter related thrombosis.

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Machine Learning and Data Mining in Diabetes Diagnosis and Treatment

Cited by 13 publications

References 13 publications

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Feature Selection with PSO and Convolutional Neural Network with Long Short-Term Memory for Medical Application

The association of vitamin-D level with catheter-related-thrombosis in hemodialysis patients: A data mining model

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