Machine learning algorithms for Diabetes prediction and neural network method for blood glucose measurement

Bavkar, Vandana C.; Shinde, Arundhati

doi:10.17485/ijst/v14i10.2187

Cited by 22 publications

(6 citation statements)

References 27 publications

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“…In the annals of modern healthcare research, the strategic deployment of machine learning to grapple with the monumental challenge of diabetes classification has unfailingly occupied a spotlight [6]. The intrigue and allure of this intersection between computational prowess and medical insight have galvanized countless researchers to charter previously unexplored terrains.…”

Section: Related Workmentioning

confidence: 99%

“…Vandana Bavkar, with an academic rigor that's now cited extensively, delivered a magnum opus-a systematic review that scrutinized the versatile applications of machine learning, data mining techniques, and tools in the expansive canvas of diabetes research [6]. His explorations weren't just confined to the realms of prediction and diagnosis.…”

Section: Related Workmentioning

confidence: 99%

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Machine Learning Techniques for Diabetes Classification: A Comparative Study

Mustafa,

Mohamed,

Nabil

et al. 2023

IJACSA

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In light of the growing global diabetes epidemic, there is a pressing need for enhanced diagnostic tools and methods. Enter machine learning, which, with its data-driven predictive capabilities, can serve as a powerful ally in the battle against this chronic condition. This research took advantage of the Pima Indians Diabetes Data Set, which captures diverse patient information, both diabetic and non-diabetic. Leveraging this dataset, we undertook a rigorous comparative assessment of six dominant machine learning algorithms, specifically: Support Vector Machine, Artificial Neural Networks, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes. Aiming for precision, we introduced principal component analysis to the workflow, enabling strategic dimensionality reduction and thus spotlighting the most salient data features. Upon completion of our analysis, it became evident that the Random Forest algorithm stood out, achieving an exemplary accuracy rate of 98.6% when 'BP' and 'SKIN' attributes were set aside. This discovery prompts a crucial discussion: not all data attributes weigh equally in their predictive value, and a discerning approach to feature selection can significantly optimize outcomes. Concluding, this study underscores the potential and efficiency of machine learning in diabetes diagnosis. With Random Forest leading the pack in accuracy, there's a compelling case to further embed such computational techniques in healthcare diagnostics, ushering in an era of enhanced patient care.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Machine Learning Techniques for Diabetes Classification: A Comparative Study

Mustafa,

Mohamed,

Nabil

et al. 2023

IJACSA

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“…Bavkar et al [10] have designed a pipeline based model using deep learning (DL) techniques to predict diabetes. It includes data augmentation using a variable auto encoder (VAE), feature augmentation using sparse encoder (SAE), and a convolution neural network for classification.…”

Section: Related Workmentioning

confidence: 99%

Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation

Ahamed

Arya

Nancy

2022

Advances in Human-Computer Interaction

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The technical improvements in healthcare sector today have given rise to many new inventions in the field of artificial intelligence. Patterns for disease identification are carried out, and the onset of prediction of many diseases is detected. Diseases include diabetes mellitus disease, fatal heart diseases, and symptomatic cancer. There are many algorithms that have played a critical role in the prediction of diseases. This paper proposes an ML based approach for diabetes mellitus disease prediction. For diabetes prediction, many ML algorithms are compared and used in the proposed work, and finally the three ML classifiers providing the highest accuracy are determined: RF, GBM, and LGBM. The accuracy of prediction is obtained using two types of datasets. They are Pima Indians dataset and a curated dataset. The ML classifiers LGBM, GB, and RF are used to build a predictive model, and the accuracy of each classifier is noted and compared. In addition to the generalized prediction mechanism, the data augmentation technique is also used, and the final accuracy of prediction is obtained for the classifiers LGBM, GB, and RF. A comparative study and demonstration between augmentation and non-augmentation are also discussed for the two datasets used in order to further improve the performance accuracy for predicting diabetes disease.

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“…We used the type 1 diabetes mellitus, 35 type 2 diabetes mellitus, 36 and GDM 37 datasets, which were obtained from previously published literature [38][39][40][41][42][43][44][45][46][47][48] for training, testing, and validation. More details about the data are shown in Table 2.…”

Section: Data Collectionmentioning

confidence: 99%

“…This dataset originally belonged to the National Institute of Diabetes and Digestive and Kidney Diseases. The various research [45][46][47][48] was done by using this dataset to diagnose whether a patient is diabetic or not. All its features are shown in Figure 3.…”

Section: It Is An Inpatient Encounter (A Clinic Confirmation)mentioning

confidence: 99%

Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification

Nagaraj¹,

Deepalakshmi²,

Mansour³

et al. 2021

DMSO

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Purpose Classification of medical data is essential to determine diabetic treatment options; therefore, the objective of the study was to develop a model to classify the three diabetes type diagnoses according to multiple patient attributes. Methods Three different datasets are used to develop a novel medical data classification model. The proposed model involved preprocessing, artificial flora algorithm (AFA)-based feature selection, and gradient boosted tree (GBT)-based classification. Then, the processing occurred in two steps, namely, format conversion and data transformation. AFA was applied for selecting features, such as demographics, vital signs, laboratory tests, medications, from the patients’ electronic health records. Lastly, the GBT-based classification model was applied for classifying the patients’ cases to type I, type II, or gestational diabetes mellitus. Results The effectiveness of the proposed AFA-GBT model was validated using three diabetes datasets to classify patient cases into one of the three different types of diabetes. The proposed model showed a maximum average precision of 91.64%, a recall of 97.46%, an accuracy of 99.93%, an F-score of 94.19%, and a kappa of 96.61%. Conclusion The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently.

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Machine learning algorithms for Diabetes prediction and neural network method for blood glucose measurement

Cited by 22 publications

References 27 publications

Machine Learning Techniques for Diabetes Classification: A Comparative Study

Machine Learning Techniques for Diabetes Classification: A Comparative Study

Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation

Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification

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