Traditional Diet-Based Nutrition Recommendation System using Data Analysis

Personalized dietary planning has become increasingly important in modern healthcare due to the rapid rise in chronic diseases such as obesity, diabetes, hypertension, and cardiovascular disorders. Traditional diet planning methods are generally based on standardized guidelines and manual consultations, which often fail to consider individual health parameters, lifestyle differences, and nutritional requirements. This limitation highlights the need for an intelligent and data-driven system that can provide accurate, customized, and practical diet recommendations. The primary objective of this study is to develop a Traditional Diet-Based Nutrition Recommendation System using data analysis and machine learning techniques that integrates traditional dietary knowledge with modern computational approaches to enhance personalized nutrition planning. The proposed system analise various user-specific health parameters, including age, gender, body mass index (BMI), disease type and severity, physical activity level, and dietary restrictions, to generate tailored diet recommendations. The methodology involves systematic data collection from reliable sources, followed by data preprocessing techniques such as handling missing values, removing inconsistencies, encoding categorical variables, and normalization to ensure data quality and consistency. Multiple machine learning algorithms, including Decision Tree, Random Forest, and Support Vector Machine, are implemented to classify users into appropriate diet categories. Model evaluation is conducted using performance metrics such as accuracy, precision, recall, and F1-score, along with cross-validation techniques to improve model reliability and robustness. The experimental results demonstrate that advanced models such as Random Forest and Support Vector Machine significantly outperform the traditional Decision Tree model in terms of prediction accuracy and overall performance. The system effectively provides structured, interpretable, and personalized dietary recommendations that support healthy lifestyle management and chronic disease prevention. The proposed framework emphasizes the importance of combining traditional dietary practices with modern data analysis techniques and offers scalability for future integration into web-based and real-time healthcare applications.