A Novel Approach to Multi-Disease Detection in Healthcare using CNN, Random Forest & XGBoost

In recent years, there has been a significant rise in the recognition and adoption of artificial intelligence and machine learning. The COVID-19 pandemic has accelerated this trend, leading to a rapid digital transformation and the adoption of disruptive technology across industries. One sector that has seen tremendous benefits from these technologies is healthcare, where AI, machine learning, and deep learning have become crucial components. Deep learning has had a significant impact on healthcare by enabling improved patient monitoring and diagnostics. Against this backdrop, our project aims to use AI to detect seven diseases in a few clicks from the comfort of one's home. This approach has a high level of accuracy and eliminates the need to wait for days for test results, enabling quick treatment of the diseases. The first four diseases, Alzheimer's, Brain Tumor, COVID-19 & Pneumonia will be detected using CNN, a machine-learning algorithm that assigns learnable biases and weights to various objects or aspects within an image, to distinguish them from one another. The remaining three diseases, Breast Cancer, Diabetes & Heart Disease will be detected using two classical machine learning algorithms, Random Forest and XGBoost. As more datasets become available, the accuracy of the project is expected to improve. Moreover, the project can possibly extend and distinguish different illnesses later on. The advantages of such an AI-based infection discovery framework are gigantic. It could empower individuals to recognize infections early, even before they become suggestive, and empower early treatment, which might actually save lives. The comfort of having the option to identify diseases from home could likewise urge more individuals to get tested, which could additionally upgrade sickness counteraction endeavours. Generally, this venture is a promising illustration of how machine learning and AI can be utilized to further develop medical services results.