A Web portal to predict whether the person is diabetic, prediabetic or healthy and then provide him personalized prelimnary treatment in the form of diet and exercise plan. The name of the web portal is Myfitness. The system is made for 3 users mainly Patient, Doctor and Admin. The function of Admin is to Add new Doctors in the system. The function of Doctor is to view Appointments booked by the Patient. The Patient needs to register and Login to the system. After successfully login the user needs to fill up an Analysis form so that prediction can take place. Everytime the user fills up the Analysis form the data is given to Machine learning model and prediction takes place. I have used KNN model to predict weather the person is diabetic, prediabetic or healthy. The patient can also get the visualization of the input and the output parameters. The data set is taken from kaggle.

A smart disease prediction system leveraging Random Forest and Neural Networks, achieving 92% accuracy in diagnosis predictions. Integrated a custom diet recommendation engine, providing personalized nutrition plans that adapt to patient health profiles.

The Medicine Recommendation System predicts diseases based on user symptoms and suggests relevant workouts and diet plans. It utilizes machine learning for prediction and displays recommendations through an interactive Bootstrap UI. The system aims to improve health management by providing tailored lifestyle suggestions.

Technologies Used: Python, Flask, HTML, CSS, JavaScript, Bootstrap, SQLite, Machine Learning (Scikit-learn, NumPy, Pandas) Description: A complete healthcare support system that provides disease prediction, symptoms analysis, precautions, description, suggested workouts, and diet plans. The system also includes a feedback form and a blog section.

Fit AI is an AI-powered fitness management system that provides personalized workout plans, diet recommendations, and health analytics. It integrates deep learning(Vision Transformer) for body analysis and prediction, along with a modern frontend and scalable backend architecture to deliver intelligent, data-driven fitness insights.

A Web portal to predict whether the person is diabetic, prediabetic or healthy and then provide him personalized prelimnary treatment in the form of diet and exercise plan. The name of the web portal is Myfitness. The system is made for 3 users mainly Patient, Doctor and Admin. The function of Admin is to Add new Doctors in the system. The function of Doctor is to view Appointments booked by the Patient. The Patient needs to register and Login to the system. After successfully login the user needs to fill up an Analysis form so that prediction can take place. Everytime the user fills up the Analysis form the data is given to Machine learning model and prediction takes place. I have used KNN model to predict weather the person is diabetic, prediabetic or healthy. The patient can also get the visualization of the input and the output parameters. The data set is taken from kaggle.

The Diet Plan Prediction System is a Python-based mini project using pandas that provides personalized diet recommendations. Users input details like age, gender, weight, height, food preference, and fitness goals (weight loss, gain, or maintenance). The system calculates nutritional needs and filters food items from a dataset accordingly.

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PerformedETLandEDAusingPython, reducing the features to70%usingPCAand feature selection throughchi-Squareandt-test, resulting in a3%increase in Accuracy◦Implemented various training models, includingSVM, Naive Bayes, Random Forest, Decision Trees, SVMKernel, and Logistic Regression, withRandom Forest achieving the highest accuracy of89

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Personalized Diet Recommendation System with Calorie Prediction and Meal Planning

Personalized Nutrition System V2 - AI-powered health analysis with blood report OCR, ML predictions, and diet planning

ML-based disease prediction system using SVM. Analyzes symptoms to provide diagnoses, medications, diet plans, and workout recommendations.

Develop a personalized food recommendation system Use Machine Learning for diet prediction Consider: Age, weight, height Activity level Disease & allergies Generate: Diet plan Recommended foods Health warnings Provide downloadable PDF report

The "Diet Recommendation System" project utilizes Random Forest for dietary pattern prediction and K-means clustering for personalized food recommendations. By analyzing user preferences and nutritional data, it suggests tailored diet plans, fostering healthier eating habits.

Diabetes Prediction & AI-Driven Diet Recommendation System This project leverages machine learning and data science principles to predict diabetes risk based on medical report inputs and recommend personalized dietary plans.

The Personalized Health Plans system allows users to input personal details, upload medical reports, and receive AI-generated disease predictions. The ML model extracts relevant health data, summarizes it, and predicts potential diseases along with severity and stage. Based on the predictions, the system provides a personalized diet and lifestyle p

HealthHub is a smart healthcare app with an inbuilt heart disease prediction system, personalized diet plans, early-stage treatment tips, doctor consultations, and a medicine reminder feature—designed to be an all-in-one digital caretaker.

A machine learning project that classifies food items based on nutritional attributes such as calories, protein, fat, and carbohydrates. The system uses multiple ML models, selects the best-performing classifier, and supports real-time prediction through a Streamlit application for diet planning and health monitoring.

Developed an AI-based diet recommendation system using deep learning and machine learning models to generate personalized meal plans based on user health goals, dietary preferences, and nutrient requirements. Integrated Flask for deployment and optimized prediction accuracy through data preprocessing and model tuning for better nutrition outcomes.

This project proposes a model which primarily focuses on having easy diagnosis and prediction of the disease. That is to provide remote diagnosis to the patients. In this suggested system the user can provide the input either by speech or entering directly into the UI. The proposed model once after detecting the disease also displays the description of the same and displays a more informed link for further elucidation. Along, with it the user is also recommended important health tips, balanced diet plan, and required exercises which will help him to lead a potential healthy lifestyle.