Human nutrient deficiency prediction using machine learning

Project Code :TCMAPY2044

Objective

The objective of this project is to develop a machine learning-based system that predicts nutrient deficiencies such as Iron, Vitamin C, Vitamin B12, and Vitamin D based on symptoms. The system will be built using Decision Tree, Random Forest, and XGBoost algorithms for accurate prediction. The tool will serve as an easy-to-use web application powered by Flask, providing instant feedback to users. It aims to bridge the gap between clinical testing and at-home self-assessment. The system will provide users with an automated prediction to raise awareness about their potential nutrient deficiencies. Ultimately, the goal is to enhance early detection and prevention through accessible technology.

Abstract

This project aims to predict nutrient deficiencies, including Iron, Vitamin C, Vitamin B12, and Vitamin D, based on symptoms observed in individuals. Using machine learning algorithms like Decision Tree, Random Forest, and XGBoost, the system analyses symptom data to predict the deficiency type. The dataset is sourced from Kaggle, and the model is trained on various symptom-feature data. The web application built with Flask serves as the user interface, allowing individuals to input their symptoms and receive predictions. The system enhances health awareness and provides a fast, accessible tool for early detection. With this tool, individuals can make informed decisions about their nutritional health. The project combines healthcare with advanced machine learning techniques, focusing on accuracy and usability. The system is expected to provide a simple and effective solution for predicting nutrient deficiencies.

Keywords: Nutrient Deficiency Prediction, Machine Learning, Decision Tree, Random Forest, XGBoost, Symptom-Based Diagnosis, Flask, Healthcare Technology, Nutritional Health Monitoring.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.