International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025
p-ISSN: 2395-0072
www.irjet.net
Automated Dietary Recommendation System using Machine Learning Katta Sreeja1, Lekkala Sathwika2, Gubba Varshith3 , Dr. A. S. Narasimha Raju 4 1B. Tech Student, Dept. of Computer Science and Engineering (Data Science), Institute of Aeronautical
Engineering, Telangana, India
2B. Tech Student, Dept. of Computer Science and Engineering (Data Science), Institute of Aeronautical
Engineering, Telangana, India B. Tech Student, Dept. of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering, Telangana, India 4Assistant Professor, Dept. of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering, Telangana, India ---------------------------------------------------------------------***--------------------------------------------------------------------3
Abstract - —Dietary suggestions specific to each person’s needs are becoming more and more significant as the market for individualized health solutions expands. This study presents the Automated Dietary Recommendation System (ADRS), which generates customized meal recommendations by using a Random Forest classifier. To provide the best dietary recommendations, the algorithm takes into consideration a number of user-specific parameters, including dietary preferences, allergies, and medical problems. The system predicts appropriate food products that correspond with users’ health objectives by training the Random Forest model on a dataset that includes food nutrients and user health profiles. Classification accuracy and user satisfaction measures are used to assess the model’s performance, and the findings indicate that the model can produce nutritional recommendations that are both accurate and effective. Our ADRS provides a flexible and scalable solution that may help users keep a balanced and healthy diet.
techniques, particularly the Random Forest classifier, to provide personalized meal suggestions based on userspecific health data. The Random Forest algorithm, known for its robustness and classification accuracy, is employed to analyze and predict optimal dietary options based on a range of factors such as age, gender, medical conditions, and individual preferences. This approach allows the system to generate tailored diet plans that are adaptable to the user’s unique needs, contributing to improved health outcomes. The primary objectives of this study are to develop an automated system that delivers personalized meal recommendations, to employ the Random Forest classifier for accurate food item predictions, and to assess the system’s performance in terms of classification accuracy, user satisfaction, and its potential to promote healthier eating habits. This research aims to provide an effective and scalable solution that can assist users in maintaining a balanced and healthy diet. The rest of the paper is structured as follows: a review of existing dietary recommendation systems is provided, followed by a detailed explanation of the methodology and dataset used. The results and performance evaluation are then presented, and the paper concludes with a discussion on the system’s potential impact and future developments
Key Words: Diet, BMI, Calories, Diseases, Machine Learning, Random Forest. 1.INTRODUCTION In recent years, the rising prevalence of lifestyle-related diseases such as obesity, diabetes, and cardiovascular conditions has significantly increased the demand for personalized health management solutions. Among the various strategies to address these health challenges, dietary interventions play a crucial role in preventing and managing chronic diseases. However, designing a suitable diet that aligns with an individual’s health profile, preferences, and dietary restrictions is often complex and requires expert guidance. Traditional dietary recommendation methods tend to be manual and time consuming, lacking the ability to scale for diverse individual requirements. With the advancements in Artificial Intelligence (AI) and Machine Learning (ML), the opportunity to automate and personalize dietary planning has emerged as a promising solution to these challenges.
2. LITERATURE SURVEY [1],”A Novel Time-Aware Food Recommender System Based on Deep Learning and Graph Clustering” builds a smarter, more personalized way to recommend food. It com- bines what users like with the actual content of the food, while also considering factors like the time of day, social circles, and trust networks. The goal is to provide healthier and more tailored food suggestions. However, many existing systems fall short by ignoring key factors like the ingredients in food, how people’s preferences change over time, and the challenge of recommending food to new users or for new items. These issues make it harder to offer truly personalized and effective recommendations for healthier eating.
In this research, we introduce an Automated Dietary Recommendation System (ADRS), utilizing machine learning
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[2],”Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review” looks
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