Predictions of Diabetes and Diet Recommendation System for Diabetic Patients using Machine Learning Techniques

Bhat, Salliah Shafi; Ansari, Gufran Ahmad

doi:10.1109/incet51464.2021.9456365

Cited by 19 publications

(11 citation statements)

References 12 publications

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“…Furthermore, applying deep learning algorithms to predict serum PLP concentration solely based on dietary intake reveals the importance of AI in nutrition assessment and disease prevention. All these studies collectively suggest that AI can reshape clinical nutrition in the future, offering personalized interventions and predictive capabilities for disease prevention and management [42][43][44][45][46][47][48][49].…”

Section: Discussionmentioning

confidence: 99%

“…Bond et al [47] provide personalized interventions, assisting disease prevention and management and addressing ethical and regulatory concerns. Bhat and Ansari [48] were able to create machine learning techniques to predict diabetes and recommend proper diets for diabetic patients. The authors emphasize the importance of data analysis in healthcare, and they were able to present a model for diabetic prediction and diet recommendation.…”

Section: Predictive Modeling For Diseasementioning

confidence: 99%

“…Food recognition is more effective when it follows dietary assessment because it facilitates the user's decision on food choices and considers user expectations and requirements after the evaluation. Furthermore, patients with specific medical conditions can use AI techniques for disease-predictive modeling, diagnosis, and monitoring [42][43][44][45][46][47][48][49][50][51][52]. The application of AI aims to prevent and control disease development in the human body from a nutritional perspective.…”

Section: Conceptual Framework For Applying Ai ML and Dl In Nutritionmentioning

confidence: 99%

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Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

Theodore Armand,

Nfor,

Kim

et al. 2024

Nutrients

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In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.

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Section: Discussionmentioning

confidence: 99%

Section: Predictive Modeling For Diseasementioning

confidence: 99%

Section: Conceptual Framework For Applying Ai ML and Dl In Nutritionmentioning

confidence: 99%

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Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

Theodore Armand,

Nfor,

Kim

et al. 2024

Nutrients

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“…With Voting, it employs an ensemble of machine learning approaches [2]. On medical data, supervised machine learning methods such as Voting Classifier and Random Forest are used to construct a model that can discriminate between accident and normal instances [9]. Three experiments were trained and assessed using a combination of random forest-based methods and a variety of current methodologies.…”

Section: Discussionmentioning

confidence: 99%

“…As a consequence of our varied research portfolio, a number of multidisciplinary projects have been published as a result of our efforts [8]. In this work, we suggest a narrative approach for uncovering key qualities using machine learning algorithms, which improves cardiovascular disease prediction accuracy [9]. A model is proposed that incorporates a variety of attributes and classification approaches.…”

Section: Introductionmentioning

confidence: 99%

Diabetes Prediction Using Blood Sample Data with Novel Voting Classifier over Random Forest

Ramakrishnan

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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This study focuses on how to predict diabetes using blood sample data and machine learning algorithms like the Voting Classifier over the Random Forest technique. The proposed prediction models were trained and evaluated on a dataset that included seven variables: glucose level, diastolic blood pressure, blood thickness, insulin levels, BMI, age, and skin. The new Voting classifier (VC) and Random Forest (RF) algorithms are used on a diabetes dataset of 1495 records with 10 features, sample size=5, and two groups with a g-power value of 80%. With a threshold of 0.05, a confidence interval of 95 percent, and a standard deviation of one standard deviation, the patients’ information was acquired from a variety of websites. The framework was built using blood sample data and the VC over Random Forest machine learning algorithm, resulting in a successful research of diabetes prediction using blood sample data and the Voting Classifier (95%) over Random Forest machine learning technique (85 percent). With a 95 percent confidence interval, the two-tailed t-test revealed a statistical significance value of 0.001 (p0.05). This research shows that the VC algorithm’s results are more accurate than the RF approach, which was written in Python.

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A Domain Oriented Framework for Prediction of Diabetes Disease and Classification of Diet Using Machine Learning Techniques

Bhat

Ansari

2023

Advanced Technologies and Societal Change

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Predictions of Diabetes and Diet Recommendation System for Diabetic Patients using Machine Learning Techniques

Cited by 19 publications

References 12 publications

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

Diabetes Prediction Using Blood Sample Data with Novel Voting Classifier over Random Forest

A Domain Oriented Framework for Prediction of Diabetes Disease and Classification of Diet Using Machine Learning Techniques

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