Statistical Study on Disease Recognition by Face

Artificial Intelligence (AI) has become an important tool in modern healthcare for improving disease diagnosis and early detection. This project focuses on developing a Disease Recognition Model using Artificial Intelligence and statistical analysis of human facial features. The main objective of the study is to identify possible health risks by analyzing measurable facial characteristics. Research studies suggest that certain diseases may cause subtle changes in facial structure and appearance. By using advanced computational methods, these facial patterns can be analyzed to support early disease detection. The proposed system uses facial images as input, which are captured through an uploaded photograph. The face is first detected using advanced computer vision techniques such as Convolutional Neural Networks (CNNs) or MediaPipe facial landmark detection. After detecting the face, important facial measurements are extracted from specific landmark points. These measurements include features such as eye size, ear length, eye-to-ear distance, nose length, jaw length, upper lip size, and forehead height. These facial features are then organized into a structured dataset for further analysis. Statistical analysis methods are applied to examine the relationship between facial measurements and the possible risk of certain diseases. In addition, machine learning and deep learning models such as CNNs, transfer learning models, or hybrid AI models are used to classify patterns and predict disease risk. The model aims to identify whether specific facial characteristics are associated with conditions such as heart disease, liver disease, or hormonal imbalance. One of the major advantages of this system is that it provides a non-invasive and cost-effective approach for preliminary health screening. Unlike traditional medical tests, this method only requires a facial image to analyze potential health indicators. The integration of artificial intelligence with facial feature analysis can help support healthcare professionals in early diagnosis and preventive healthcare strategies. The results of this study can contribute to the development of intelligent healthcare systems that assist doctors in identifying disease risks more quickly and efficiently. Although the system does not replace medical diagnosis, it can serve as a supportive tool for early detection and health monitoring. This research demonstrates the potential of combining artificial intelligence, computer vision, and statistical analysis to build innovative solutions for modern healthcare applications.