Optimized Food Recognition System for Diabetic Patients

Anusha, B.; Sabena, S.; SaiRamesh, L.

doi:10.1007/978-981-10-8657-1_38

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…The focus of this study is to review various diabetes detection mechanisms on the Type 3 diabetes to identify this, Naive Bayes (NB), SVM, and DT classification methods are employed and assessed on Pima Indian Diabetic dataset (PIDD). Using several criteria, the experimental performances of these algorithms are compared, and they all attain good accuracy [1].This study examines the familiar metrics such as F1score, Precision, Recall and the Accuracy rate of various diabetes detection mechanisms [2].…”

Section: Types Of Diabetesmentioning

confidence: 99%

Comparative Analysis of Predicting the Diabetic Disease Using Machine Learning Techniques

Revathy¹,

Selvanayagi²

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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Machine Learning is concerned with the making of calculations and methods that use PCs to learn and acquire insight, using the related knowledge available.This work is focused on machine learning approaches for predicting diabetic disorders, using datasets from Predict the Diabetic Diseases. A web-based comparative analysis of multiple machine learning algorithms (Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression) is utilized in this paper, to assess their performances in recognizing reliable models for detecting diabetic disease. To see the effects of adding more features to the classification model, three performance measures were chosen: F1-Measure, Precision, and Accuracy.

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Section: Types Of Diabetesmentioning

confidence: 99%

Comparative Analysis of Predicting the Diabetic Disease Using Machine Learning Techniques

Revathy¹,

Selvanayagi²

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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“…SVM uses kernel tricks to convert the features from low to high dimension for effective classification [10]. SVM defines the input in the combination of features and its class labels i.e.…”

Section: Support Vector Machinementioning

confidence: 99%

Support Vector Machine-Based Scheme Identification System for the Marginalized People

Varghese

JanarthananPillai

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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Scheme identification system is required to improve the socio-economic status of marginalized people. The absence of technological innovations creates more complications during online scheme identifications for the development of marginalized people. In order to increases the service availability to marginalized people, a scheme identification system is proposed. In this, the previous beneficiary data collected from Kerala state SC, women development and Handicapped Persons cooperation are used. The Support Vector Machine (SVM) with linear and non-linear kernels identifies the best suitable schemes based on the input features. Finally, SVM with Gaussian Radial Basis Function (RBF) kernel achieved 91.23 %, 95.65% and 94.23% accuracy during scheme identification for SC, Women development and Handicapped Persons cooperation data.

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Optimized Food Recognition System for Diabetic Patients

Cited by 2 publications

References 38 publications

Comparative Analysis of Predicting the Diabetic Disease Using Machine Learning Techniques

Comparative Analysis of Predicting the Diabetic Disease Using Machine Learning Techniques

Support Vector Machine-Based Scheme Identification System for the Marginalized People

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