Prediction of Type-2 Diabetes using Classification and Ensemble Method Approach

Goyal, Priyanka; Jain, Somil

doi:10.1109/mecon53876.2022.9752268

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…From Table 5 and Figure 5, we can find that both PCA and IG (in the case of the top five features) increase the model performance to a very small extent. Although this study has shown a good level of accuracy in classifying diabetes compared to [21][22][23], the accuracy was not satisfactory for all groups. The large decrease in the performance of Group 7 and Group 8 implies that the inclusion of clinical features/factors is significant, and that glucose level is especially significant as a clinical factor for predicting diabetes.…”

Section: Discussioncontrasting

confidence: 60%

“…On the other hand, a large number of researchers have evaluated the widely used Pima Indian Diabetes Database (PIDD), and those studies have not achieved a satisfactory level of prediction accuracy. Using PIDD as an example, Chatrati et al [21] achieved 75% accuracy, Jashwanth Reddy et al [22] attained 80% accuracy, and Goyal and Jain [23] accomplished 77% accuracy. Moreover, there is a dearth of trustworthy and relevant labeled data for diabetes prediction research in least-developed countries, such as Bangladesh [24].…”

Section: Related Workmentioning

confidence: 99%

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Data-Driven Diabetes Risk Factor Prediction Using Machine Learning Algorithms with Feature Selection Technique

2023

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As type 2 diabetes becomes more prevalent across the globe, predicting its sources becomes more important. However, there is a big void in predicting the risk factors of this disease. Thus, the purpose of this study is to predict diabetes risk factors by applying machine learning (ML) algorithms. Two-fold feature selection techniques (i.e., principal component analysis, PCA, and information gain, IG) have been applied to boost the prediction accuracy. Then, the optimal features are fed into five ML algorithms, namely decision tree, random forest, support vector machine, logistic regression, and KNN. The primary data used to train the ML model were collected based on the safety procedure described in the Helsinki Declaration, 2013, and 738 records were included in the final analysis. The result has shown an accuracy level of over 82.2%, with an AUC (area under the ROC curve) value of 87.2%. This research not only identified the most important clinical and nonclinical factors in diabetes prediction, but it also found that the clinical risk factor (glucose) is the most relevant for diabetes prediction, followed by dietary factors. The noteworthy contribution of this research is the identification of previously unclassified factors left over from the previous study that considered both clinical and non-clinical aspects.

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Section: Discussioncontrasting

confidence: 60%

Section: Related Workmentioning

confidence: 99%

Data-Driven Diabetes Risk Factor Prediction Using Machine Learning Algorithms with Feature Selection Technique

2023

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“…For example, "Regression algorithms" predict outcomes and make forecasts by estimating the relationship between values. "Two class classifiers" answer simple two-choice questions, e.g., yes or no, true or false [29]. For forecasting problems, logistic regression and linear regression can be used to predict data.…”

Section: Machine Learning Algorithms For Optimizationmentioning

confidence: 99%

A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing

et al. 2022

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With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration of sensors, Internet-of-things (IoT), cloud computing, and data analytics leading to the process of digitization. However, computer-aided design (CAD) is used to generate G codes for different process parameters to input to the 3D printer. To automate the whole process, in this study, a customer-driven CPS framework is developed to utilize customer requirement data directly from the website. A cloud platform, Microsoft Azure, is used to send that data to the fused diffusion modelling (FDM)-based 3D printer for the automatic printing process. A machine learning algorithm, the multi-layer perceptron (MLP) neural network model, has been utilized for optimizing the process parameters in the cloud. For cloud-to-machine interaction, a Raspberry Pi is used to get access from the Azure IoT hub and machine learning studio, where the generated algorithm is automatically evaluated and determines the most suitable value. Moreover, the CPS system is used to improve product quality through the synchronization of CAD model inputs from the cloud platform. Therefore, the customer’s desired product will be available with minimum waste, less human monitoring, and less human interaction. The system contributes to the insight of developing a cloud-based digitized, automatic, remote system merging Industry 4.0 technologies to bring flexibility, agility, and automation to AM processes.

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