Search Topic

Machine learning applications in personalized nutrition for chronic disease management. I want articles that use machine learning to tailor nutritional advice for managing chronic diseases.

Summary

The literature search identified several key studies demonstrating the use of machine learning to tailor nutritional advice for managing chronic diseases, with references [1, 2, 4, 5, 7] being most directly relevant.
These studies showcase a range of machine learning techniques and applications aimed at personalized nutrition for chronic disease management. For instance, [1] develops deep learning-based software using biomarkers to tailor dietary recommendations for enhanced patient management, highlighting a sophisticated approach to personalized nutrition. [2] introduces an efficient diet recommendation system that leverages multiple machine learning models, focusing on disease-based food recommendations. Additionally, [4] and [5] specifically target diabetes patients, with [4] using Restricted Boltzmann Machines for dietary and exercise recommendations and [5] utilizing neural networks for food segmentation and nutritional estimation. [7] evaluates machine learning for personalized diabetes management in a remote monitoring program, demonstrating practical application. These studies collectively illustrate the effectiveness and variety of machine learning applications in personalized nutrition, underscoring the potential of these technologies in managing chronic diseases through tailored dietary advice.
To understand the relationships and patterns within the papers found, see also:
So far, I've closely analyzed 100 of the most promising papers, and I've found ~10-17 that are relevant, which is probably more than 90% of all that exist.
To get this estimate, we do a statistical analysis of the discovery process.

References

Show only:
Last 5 years
Last 2 years
> 1 citation per year
> 5 citations per year
Topic Match
Cit./Year
Year
Paper
Paper Relevance Summary

99.8%
3.4
2021
[1] Biomarker-based deep learning for personalized nutrition Dimitrios P. Panagoulias, ..., and G. Tsihrintzis 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) 2021 - 9 citations - Show abstract - Cite 99.8% topic match
Develops deep learning-based personalized nutrition software. Utilizes biomarkers to tailor dietary recommendations, enhancing patient management. Demonstrates efficacy through evaluation on real data, indicating high performance.
Develops deep learning-based personalized nutrition software. Utilizes biomarkers to tailor dietary recommendations, enhancing patient management. Demonstrates efficacy through evaluation on real data, indicating high performance.

99.6%
20.7
2020
[2] Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model C. Iwendi, ..., and F. Noor IEEE Access 2020 - 91 citations - Show abstract - Cite 99.6% topic match
Introduces a machine learning-assisted diet recommendation system. Utilizes logistic regression, naive bayes, RNN, MLP, GRU, and LSTM for tailoring patient diets. Focuses on disease-based food recommendations influenced by patient-specific data like age and dietary needs.
Introduces a machine learning-assisted diet recommendation system. Utilizes logistic regression, naive bayes, RNN, MLP, GRU, and LSTM for tailoring patient diets. Focuses on disease-based food recommendations influenced by patient-specific data like age and dietary needs.

98.4%
1.3
2023
[3] Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables Agustin Martin-Morales, ..., and Michihiro Araki Nutrients 2023 - 1 citations - Show abstract - Cite 98.4% topic match
Provides ML-based analysis of dietary and health data for CVD. Random forests algorithm used to predict CVD mortality from health/nutrition variables. Emphasizes dietary intake's importance, suggesting potential for personalized nutrition advice.
Provides ML-based analysis of dietary and health data for CVD. Random forests algorithm used to predict CVD mortality from health/nutrition variables. Emphasizes dietary intake's importance, suggesting potential for personalized nutrition advice.

97.3%
0.0
2022
[4] RDED: Recommendation of Diet and Exercise for Diabetes Patients using Restricted Boltzmann Machine Muhammad Sajid, ..., and Muhammad Fuzail VFAST Transactions on Software Engineering 2022 - 0 citations - Show abstract - Cite 97.3% topic match
Utilizes Restricted Boltzmann Machines for dietary and exercise recommendations. Targets diabetes patients by tailoring nutrition and activity based on medical data and preferences. Focuses on personalization through user-food ratings, impacting chronic disease management.
Utilizes Restricted Boltzmann Machines for dietary and exercise recommendations. Targets diabetes patients by tailoring nutrition and activity based on medical data and preferences. Focuses on personalization through user-food ratings, impacting chronic disease management.

95.5%
0.0
2023
[5] Visual Food Intake Monitoring System for Diabetes Management Ambika Jadoonanan and Sean Rocke 2023 5th International Conference on Bio-engineering for Smart Technologies (BioSMART) 2023 - 0 citations - Show abstract - Cite 95.5% topic match
Provides a deep learning approach to diabetes management. Utilizes neural networks for food segmentation and nutritional estimation in diabetes dietary management. Investigates incremental transfer learning for personalized diet adaptation.
Provides a deep learning approach to diabetes management. Utilizes neural networks for food segmentation and nutritional estimation in diabetes dietary management. Investigates incremental transfer learning for personalized diet adaptation.

95.4%
5.7
2023
[6] Interpretable Machine Learning for Personalized Medical Recommendations: A LIME-Based Approach Yuanyuan Wu, ..., and Mengxing Huang Diagnostics 2023 - 5 citations - Show abstract - Cite 95.4% topic match
Introduces a LIME-based machine learning system for medical advice. Targets personalized recommendations for managing heart disease and diabetes in older adults. Utilizes deep learning algorithms for interpretable health recommendations.
Introduces a LIME-based machine learning system for medical advice. Targets personalized recommendations for managing heart disease and diabetes in older adults. Utilizes deep learning algorithms for interpretable health recommendations.

95.2%
0.9
2022
[7] Improving Outcomes Through Personalized Recommendations in a Remote Diabetes Monitoring Program: Observational Study Sowmya S Kamath, ..., and Anmol Madan JMIR Formative Research 2022 - 2 citations - Show abstract - Cite 95.2% topic match
Evaluates machine learning for personalized diabetes management. Uses machine learning to predict A1c improvement and recommend actions in remote monitoring. Focuses on diabetes, a chronic disease, ensuring high relevance to tailored nutritional advice.
Evaluates machine learning for personalized diabetes management. Uses machine learning to predict A1c improvement and recommend actions in remote monitoring. Focuses on diabetes, a chronic disease, ensuring high relevance to tailored nutritional advice.

88.5%
3.8
2021
[8] Nutritional biomarkers and machine learning for personalized nutrition applications and health optimization Dimitrios P. Panagoulias, ..., and G. Tsihrintzis 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) 2021 - 11 citations - Show abstract - Cite 88.5% topic match
Employs machine learning to analyze nutritional biomarkers. Uses neural networks for general evaluation of biomarkers and predicting dietary patterns. Focuses on metabolomics to provide insights into personalized nutrition but not directly on chronic disease management.
Employs machine learning to analyze nutritional biomarkers. Uses neural networks for general evaluation of biomarkers and predicting dietary patterns. Focuses on metabolomics to provide insights into personalized nutrition but not directly on chronic disease management.

87.3%
0.0
2023
[9] Precision cardiodiet: transforming cardiac care with artificial intelligence-driven dietary recommendations Shahadat Hoshen Moz, ..., and Syed Md. Galib Radioelectronic and Computer Systems 2023 - 0 citations - Show abstract - Cite 87.3% topic match
Provides AI-driven dietary recommendations for cardiac care. Utilizes machine learning for tailored diet plans, focusing on heart health. Employs classification algorithms to identify "heart-healthy" foods.
Provides AI-driven dietary recommendations for cardiac care. Utilizes machine learning for tailored diet plans, focusing on heart health. Employs classification algorithms to identify "heart-healthy" foods.

83.0%
0.0
2023
[10] Smart E-Healthcare Application For Predicting Choronic Diseases De Silva and Ms.Gaya Thamali Dassanayake Journal Not Provided 2023 - 0 citations - Show abstract - Cite 83.0% topic match
Proposes a smart e-healthcare application using machine learning. Detects chronic diseases early through patient data analysis for prevention and treatment. Doesn't specifically focus on personalized nutrition advice for chronic disease management.
Proposes a smart e-healthcare application using machine learning. Detects chronic diseases early through patient data analysis for prevention and treatment. Doesn't specifically focus on personalized nutrition advice for chronic disease management.

67.4%
0.0
2021
[11] Διατροφικοί βιοδείκτες και μηχανική μάθηση για εφαρμογές εξατομικευμένης διατροφής και βελτιστοποίησης της ανθρώπινης υγείας Δημήτριος Παναγούλιας https://doi.org/10.26267/UNIPI_DIONE/854 2021 - 0 citations - Show abstract - Cite 67.4% topic match
Develops neural networks for personalized nutrition evaluation. Focuses on metabolomics to predict BMI and dietary patterns. Aligns with personalized nutrition but lacks chronic disease management focus.
Develops neural networks for personalized nutrition evaluation. Focuses on metabolomics to predict BMI and dietary patterns. Aligns with personalized nutrition but lacks chronic disease management focus.

64.1%
0.0
2023
[12] A Swarm Artificial Intelligence Approach for Effective Treatment of Chronic Conditions K. Kioskli and Spyridon Papastergiou 2023 19th International Conference on the Design of Reliable Communication Networks (DRCN) 2023 - 0 citations - Show abstract - Cite 64.1% topic match
Proposes a SwarmAI framework for personalized healthcare. Utilizes machine learning, including deep learning, for predictive modeling in chronic disease management. Aims for personalized intervention but lacks specific focus on nutritional advice.
Proposes a SwarmAI framework for personalized healthcare. Utilizes machine learning, including deep learning, for predictive modeling in chronic disease management. Aims for personalized intervention but lacks specific focus on nutritional advice.

55.1%
3.3
2017
[13] Live Personalized Nutrition Recommendation Engine Nitish Nag, ..., and Ramesh C. Jain Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care 2017 - 22 citations - Show abstract - Cite 55.1% topic match
Develops a machine learning-based nutrition recommendation engine. Utilizes multimedia data for personalized, context-aware dietary advice tailored to eating out. Focuses on healthy meal dish selection rather than chronic disease management directly.
Develops a machine learning-based nutrition recommendation engine. Utilizes multimedia data for personalized, context-aware dietary advice tailored to eating out. Focuses on healthy meal dish selection rather than chronic disease management directly.

54.2%
0.0
2023
[14] A Review Of The Methods And Approaches For Integrating Genetics And Metabolomics Into Personalized Nutrition No author found Journal of Pharmaceutical Negative Results 2023 - 0 citations - Show abstract - Cite 54.2% topic match
Provides a review of personalized nutrition through genetics and metabolomics. Discusses machine learning for data integration in personalized dietary interventions. Highlights ethical considerations and future research directions in personalized nutrition.
Provides a review of personalized nutrition through genetics and metabolomics. Discusses machine learning for data integration in personalized dietary interventions. Highlights ethical considerations and future research directions in personalized nutrition.

52.9%
0.0
2024
[15] EARLY PREDICTION OF LIFESTYLE DISEASES Ijsrem Journal INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 2024 - 0 citations - Show abstract - Cite 52.9% topic match
Proposes a predictive framework using machine learning for disease risk. Focuses on early prediction of lifestyle-related diseases via lifestyle data analysis. While addressing disease prevention, it does not specifically cover personalized nutrition.
Proposes a predictive framework using machine learning for disease risk. Focuses on early prediction of lifestyle-related diseases via lifestyle data analysis. While addressing disease prevention, it does not specifically cover personalized nutrition.

50.3%
3.3
2022
[16] Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning S. Oh, ..., and Jongyoul Park Journal of Personalized Medicine 2022 - 8 citations - Show abstract - Cite 50.3% topic match
Implements machine learning for personalized hypertension management. Uses reinforcement learning on electronic health records to tailor antihypertensive treatment. Focuses on hypertension in Type 2 Diabetes patients, lacking a direct nutrition focus.
Implements machine learning for personalized hypertension management. Uses reinforcement learning on electronic health records to tailor antihypertensive treatment. Focuses on hypertension in Type 2 Diabetes patients, lacking a direct nutrition focus.

47.3%
0.5
2022
[17] Personalized Nutrition for the Prevention and Treatment of Metabolic Diseases: Opportunities and Perspectives I. N. Napolsky and P. Popova Russian Journal for Personalized Medicine 2022 - 1 citations - Show abstract - Cite 47.3% topic match
Reviews personalized nutrition for metabolic disease management. Discusses use of omics technologies and data for dietary personalization. Mentions applications of computer programs in creating mobile apps for nutrition advice.
Reviews personalized nutrition for metabolic disease management. Discusses use of omics technologies and data for dietary personalization. Mentions applications of computer programs in creating mobile apps for nutrition advice.

40.7%
1.0
2022
[18] A decision tree–based classifier to provide nutritional plans recommendations Omar Aguilar-Loja, ..., and Paola A. González 2022 17th Iberian Conference on Information Systems and Technologies (CISTI) 2022 - 2 citations - Show abstract - Cite 40.7% topic match
Provides a nutritional plan recommendation model via decision trees. Uses patient data alongside BMI and BMR for tailored nutritional advice. Focuses on general nutritional habits and early disease diagnosis potential.
Provides a nutritional plan recommendation model via decision trees. Uses patient data alongside BMI and BMR for tailored nutritional advice. Focuses on general nutritional habits and early disease diagnosis potential.

38.5%
0.0
2022
[19] Machine Learning in Nutrition Research. Daniel Kirk, ..., and G. Camps Advances in nutrition 2022 - 0 citations - Show abstract - Cite 38.5% topic match
Introduces machine learning's role in nutrition research. Details ML applications in obesity, metabolic health, malnutrition, and precision nutrition. Offers framework for integrating ML in nutrition studies, lacking focus on chronic disease management.
Introduces machine learning's role in nutrition research. Details ML applications in obesity, metabolic health, malnutrition, and precision nutrition. Offers framework for integrating ML in nutrition studies, lacking focus on chronic disease management.

33.9%
1.0
2022
[20] A Model for Analysis of Diseases Based on Nutrition Deficiency Using Random Forest Chaitanya Kosaraju, ..., and Gayathri Gali 2022 7th International Conference on Communication and Electronics Systems (ICCES) 2022 - 2 citations - Show abstract - Cite 33.9% topic match
Demonstrates machine learning's role in analyzing diet-disease relationships. Uses Random Forest, DT, and KNN to link diet to non-communicable diseases prevention. Outperforms Gaussian Nave Bayes in predicting diseases from nutritional intake, achieving 0.96 accuracy.
Demonstrates machine learning's role in analyzing diet-disease relationships. Uses Random Forest, DT, and KNN to link diet to non-communicable diseases prevention. Outperforms Gaussian Nave Bayes in predicting diseases from nutritional intake, achieving 0.96 accuracy.

33.6%
0.0
2023
[21] Detection of Blood Glucose Level: A Machine Learning Approach S. Karmakar, ..., and S. Kundu 2023 7th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech) 2023 - 0 citations - Show abstract - Cite 33.6% topic match
Employs machine learning to detect blood glucose levels. Uses decision trees, SVMs, and neural networks for early detection in diabetes management. Focuses on metabolic disorder prevention, not directly on personalized nutrition advice.
Employs machine learning to detect blood glucose levels. Uses decision trees, SVMs, and neural networks for early detection in diabetes management. Focuses on metabolic disorder prevention, not directly on personalized nutrition advice.

27.2%
0.0
2023
[22] “DiagnoMe” Mobile Application for Identifying and Predicting the Chronic Diseases J. Perera, ..., and Shanika Nethusara 2023 5th International Conference on Advancements in Computing (ICAC) 2023 - 0 citations - Show abstract - Cite 27.2% topic match
Introduces a mobile app for chronic disease prediction using ML. Focuses on RNN and Neural Network models for disease identification and prediction. Lacks specificity on personalized nutrition advice for chronic disease management.
Introduces a mobile app for chronic disease prediction using ML. Focuses on RNN and Neural Network models for disease identification and prediction. Lacks specificity on personalized nutrition advice for chronic disease management.

27.1%
8.4
2021
[23] Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology. J. Morgenstern, ..., and L. Anderson Advances in nutrition 2021 - 28 citations - Show abstract - Cite 27.1% topic match
Highlights potential of big data and machine learning in nutritional epidemiology. Discusses improving dietary measurement precision and modeling diet complexity using machine learning. Lacks direct focus on personalized nutrition for chronic disease management via machine learning applications.
Highlights potential of big data and machine learning in nutritional epidemiology. Discusses improving dietary measurement precision and modeling diet complexity using machine learning. Lacks direct focus on personalized nutrition for chronic disease management via machine learning applications.

23.1%
0.6
2019
[24] Application of Unsupervised Learning in Weight-Loss Categorisation for Weight Management Programs Oladapo Babajide, ..., and Thorkild IA Sorensen 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT) 2019 - 3 citations - Show abstract - Cite 23.1% topic match
Demonstrates the use of unsupervised learning in weight management. Identifies weight loss categories in dietary intervention programs for optimized outcomes. Lacks direct application to chronic disease management or personalized nutrition advice.
Demonstrates the use of unsupervised learning in weight management. Identifies weight loss categories in dietary intervention programs for optimized outcomes. Lacks direct application to chronic disease management or personalized nutrition advice.

20.2%
0.8
2023
[25] User Input Based Health Risk Assessment to Predict Diabetes, Obesity and Heart Risk factors Preethi Salian K, ..., and V. S 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) 2023 - 1 citations - Show abstract - Cite 20.2% topic match

17.6%
1.4
2022
[26] An Extreme Value Analysis-Based Systemic Approach in Healthcare Information Systems: The Case of Dietary Intake Dimitrios P. Panagoulias, ..., and G. Tsihrintzis Electronics 2022 - 2 citations - Show abstract - Cite 17.6% topic match

16.3%
0.0
2023
[27] An Efficient Machine Learning Classification Model for Diabetes Prediction T. S. Sai Krishna, ..., and D. Haritha 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT) 2023 - 0 citations - Show abstract - Cite 16.3% topic match
Develops ML models for diabetes prediction. Utilizes three ML algorithms for improved healthcare predictive analytics. Focuses on diabetes therapy potential, not personalized nutrition advice.
Develops ML models for diabetes prediction. Utilizes three ML algorithms for improved healthcare predictive analytics. Focuses on diabetes therapy potential, not personalized nutrition advice.

16.0%
0.7
2020
[28] Predictive Modelling for Chronic Disease: Machine Learning Approach Md. Rakibul Hoque and Mohammed Sajedur Rahman Proceedings of the 2020 4th International Conference on Compute and Data Analysis 2020 - 3 citations - Show abstract - Cite 16.0% topic match

13.5%
0.0
2023
[29] A Rule-Based Expert Advisory System for Restaurants Using Machine Learning and Knowledge-Based Systems Techniques Khalid M. O. Nahar, ..., and Brij B. Gupta Int. J. Semantic Web Inf. Syst. 2023 - 0 citations - Show abstract - Cite 13.5% topic match

11.9%
0.0
2022
[30] Diet Recommendation System based on Different Machine Learners Megh Shah, ..., and Dhairya Vyas International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2022 - 0 citations - Show abstract - Cite 11.9% topic match

11.7%
0.6
2021
[31] Advancement in Precision Medicine and Recommendation System for Clinical Trials Using Deep Learning Methods A.P.Ponselvakumar, ..., and S.Swetha Jayashree IOP Conference Series: Materials Science and Engineering 2021 - 2 citations - Show abstract - Cite 11.7% topic match

9.7%
2.5
2022
[32] Development of Machine Learning Prediction Models to Explore Nutrients Predictive of Cardiovascular Disease Using Canadian Linked Population-Based Data. J. Morgenstern, ..., and Laura N. Anderson Applied physiology, nutrition, and metabolism = Physiologie appliquee, nutrition et metabolisme 2022 - 6 citations - Show abstract - Cite 9.7% topic match

9.1%
0.0
2024
[33] Machine learning's role in personalized medicine & treatment optimization Deepak Kumar, ..., and Shoumya Singh World Journal of Advanced Research and Reviews 2024 - 0 citations - Show abstract - Cite 9.1% topic match
Explores machine learning in personalized medicine and treatment optimization. Discusses analysis of genetic, lifestyle, and medical history data for tailored treatments but not nutritional advice. Highlights obstacles and ethical concerns in data sharing and use in healthcare; lacks specific focus on chronic disease nutrition management.
Explores machine learning in personalized medicine and treatment optimization. Discusses analysis of genetic, lifestyle, and medical history data for tailored treatments but not nutritional advice. Highlights obstacles and ethical concerns in data sharing and use in healthcare; lacks specific focus on chronic disease nutrition management.

7.8%
0.6
2022
[34] Multi-Disease Prediction System using Machine Learning Vaibhav Raheja, ..., and Manisha Tiwari 2022 International Conference on Futuristic Technologies (INCOFT) 2022 - 1 citations - Show abstract - Cite 7.8% topic match

7.3%
0.0
2023
[35] Analyzing and Researching the Intermediate Layer of Alliance Medical Data Combined with Edge Computing Tse-Chuan Hsu, ..., and Shyh-Wei Chen 2023 IEEE International Conference on Software Services Engineering (SSE) 2023 - 0 citations - Show abstract - Cite 7.3% topic match

7.0%
1.3
2022
[36] Diet Recommendation using Predictive Learning Approaches Anjali Jain and Alka Singhal 2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) 2022 - 2 citations - Show abstract - Cite 7.0% topic match

7.0%
0.0
2023
[37] Machine Learning-Based Approach for Early Detection and Prediction of Chronic Diseases Shubhangi S. Shambharkar, ..., and Shubhangi M. Boarkar 2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0 (IDICAIEI) 2023 - 0 citations - Show abstract - Cite 7.0% topic match

6.4%
0.0
2023
[38] Prediction of Heart Disease from the Multiple Features Available in the Database by Applying Classification Models of Machine Learning Akash Rajak and R. Panda 2023 3rd International conference on Artificial Intelligence and Signal Processing (AISP) 2023 - 0 citations - Show abstract - Cite 6.4% topic match
Utilizes machine learning to predict heart disease. Classification models analyze patient health information for treatment decisions. Lacks explicit focus on personalized nutrition for chronic disease management.
Utilizes machine learning to predict heart disease. Classification models analyze patient health information for treatment decisions. Lacks explicit focus on personalized nutrition for chronic disease management.

5.9%
0.3
2020
[39] Enhanced Predictive Learning Approaches for Tweaked Diet Proposal System in Health Care Damodharan, ..., and V. Nallarasan International Journal of Emerging Trends in Engineering Research 2020 - 1 citations - Show abstract - Cite 5.9% topic match

5.9%
0.0
2023
[40] A Food Recommender System in Academic Environments Based on Machine Learning Models Abolfazl Ajami and B. Teimourpour ArXiv 2023 - 0 citations - Show abstract - Cite 5.9% topic match

4.6%
0.0
2022
[41] Smart Diagnosis of Disease Using Machine Learning B. Rajapandian, ..., and K. Abirami 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS) 2022 - 0 citations - Show abstract - Cite 4.6% topic match

4.5%
2.6
2023
[42] Machine Learning Model for Awareness of Diet Recommendation Ajit Kumar Rout, ..., and Nandam Sri Mouli 2023 International Conference on Inventive Computation Technologies (ICICT) 2023 - 3 citations - Show abstract - Cite