Predictive Healthcare: An IoT and Machine Learning-Based Illness Prediction System

Abstract

This research explores the development of an IoT-based illness prediction system leveraging machine learning algorithms. The system aims to proactively identify potential health deteriorations by analyzing physiological data collected through wearable sensors and smart home devices. The collected data undergoes preprocessing, feature extraction, and classification using suitable machine learning models to predict the likelihood of specific illnesses. The system's effectiveness is evaluated using relevant metrics, demonstrating its potential to improve early diagnosis and personalized healthcare interventions. Results show improved prediction accuracy compared to existing methods, paving the way for timely medical intervention and enhanced patient outcomes.

Introduction

Early and accurate illness prediction is crucial for timely intervention and improved patient outcomes. Traditional healthcare relies heavily on reactive measures, often leading to delayed diagnosis and treatment. The integration of the Internet of Things (IoT) and machine learning (ML) offers a promising solution for proactive healthcare. IoT devices can continuously monitor vital signs and other health indicators, providing a rich dataset for ML algorithms to analyze and predict potential health issues. However, challenges remain in data processing, model accuracy, and ensuring data privacy and security in such systems. This project addresses these challenges by developing a robust and reliable illness prediction system.

Key Features

• Continuous data collection via wearable sensors and smart devices
• Advanced preprocessing and feature extraction for physiological signals
• Implementation of supervised machine learning models for illness prediction
• Focus on data privacy, security, and ethical handling of health data
• Evaluation with metrics such as accuracy, precision, recall, and F1-score