A Data Mining Approach to Food and Health Recommendations

Abstract

The exchange between nourishment and wellbeing is imperative for infection anticipation and in general well-being. This extend presents a information mining approach to analyze dietary propensities and suggest personalized nourishment choices custom-made to person wellbeing conditions. By utilizing huge datasets containingdietarydata,dietarydesigns,andwellbeingrecords,theframeworkutilizes progressed information mining methods such as classification, clustering, and affiliation run the show mining to reveal important experiences. The proposed framework distinguishes relationships between dietary lacks and maladies, advertisingnoteworthydietaryproposals.Ittoohighlightsunthinkablenourishments and appropriate choices based on particular wellbeing conditions, such as diabetes, weight,orcardiovascularmaladies.Comparedtoconventionalmeasurablestrategies, the framework illustrates made strides precision and proficiency in analyzing complex datasets.

Introduction

This project uses data mining and machine learning, particularly the Random Forest algorithm, to analyze the relationship between dietary habits and health conditions. It aims to provide personalized food recommendations and predict potential diseases based on a user's diet and health profile.

The system integrates data from four main sources: food nutrition data, disease data, food consumption records, and user health profiles. Through data preprocessing (e.g., cleaning, feature engineering, normalization) and classification, it offers tailored dietary advice and warns against harmful foods for users with specific health risks such as hypertension, diabetes, or obesity.

Key advantages include:

• High accuracy in disease prediction

• Personalized recommendations

• Scalability and efficiency

• Improved healthcare decision-making through data-driven insights

The architecture includes two modules:

• Admin module for managing datasets and generating insights

• User module for receiving customized dietary guidance

Preprocessing steps like data cleaning, integration, and sampling ensure data quality, while the system’s user-friendly interface makes it accessible to both individuals and healthcare providers.

Conclusion & Future Work:

The system successfully demonstrates how data mining can enhance nutritional planning and disease prevention. Future enhancements may include:

• Real-time data integration via wearable devices

• Expanded datasets for global applicability

• Advanced recommendation systems (e.g., meal plans for allergies or cultural needs)

• Testing with other ML/DL models for improved accuracy

Conclusion

This project successfully demonstrates the use of data mining techniques to analyze the intricate relationship between dietary habits and health conditions. Byleveragingdatasetscontainingfooddetails,diseaseinformation,anduserhealth data, we developed a system that provides two key functionalities: predicting potentialdiseasesbasedondietaryinputsandrecommendingsuitablefoodstailored to individual health profiles. Keyachievementsofthisprojectinclude 1) ComprehensiveDataIntegrationandAnalysis:Bycombining food,disease, anduserdata,thesystemhighlightstheinfluenceofdietarychoicesonhealth outcomes. This integration enables more personalized insights into the role of nutrition in disease prevention and management. 2) Data Preprocessing for Reliable Analysis: Data preprocessing techniques such as noise removal, missing value handling, and feature extraction ensured the quality and reliability of the input data. This step significantly improved the accuracy of the system\'s predictions and recommendations. 3) Machine Learning for Disease Prediction and Food Recommendation The use of the Random Forest algorithm provided accurate disease predictions and relevant food recommendations. 4) User-Friendly Interface: A dual dashboard design, with separate views for administrators and end-users, makes the system accessible and practical. Admins can monitor datasets and insights, while users receive .

References

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Copyright

Copyright © 2025 Mrs. Priyanka S, Mr. Ajith D, Mr. Bharathiraja V, Mr. Boopathi P, Mr. Kamalesh V, Mr. Ajay S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.