Multi Disease Detection And Diagnosis Through Ensemble Learning

Health Cure is a groundbreaking medical initiative aimed at revolutionizing early disease detection through the integration of advanced medical technology and sophisticated algorithms. This paper focuses on identifying and evaluating seven critical diseases, including COVID-19, brain tumors, breast cancer, diabetes, Alzheimer’s, pneumonia, and heart disease. The complexity of disease mechanisms and diverse symptoms poses a significant challenge in creating effective early diagnosis tools and efficient treatment plans on a global scale. This work leverages a variety of techniques, such as Random Forest, Convolutional Neural Networks (CNN), and XG Boost, to address the unmet need for accurate and timely disease diagnosis. Traditional diagnosis based on symptoms proves challenging for medical professionals to make the reliable detection of diseases a formidable task. To overcome these challenges, machine learning (ML) and deep learning algorithms plays a pivotal role in predicting and detecting high-risk diseases. The integration of supervised ML algorithms and deep learning techniques demonstrates substantial potential in outperforming existing disease diagnosis systems. After the prediction we design a user interface for users to get the test results immediately in our home just a few clicks. In this work, detect and predict seven diseases in one platform or multiple diseases under one platform using ML and DL techniques. And provide a user interface for users to get a test results immediately in our home just a few clicks after that provide a food, medicine and doctor recommendations. And our novel idea is detect heart beat rate by using heart beat sensor(MAX30100) for monitoring real time heart beat sensing in heart disease UI page, By supplying pertinent symptoms, machine learning is utilized in disease inference systems to forecast human illnesses. This paper presents a comprehensive platform capable of detecting and predicting multiple diseases using ML and DL methods.