An AI based Approach for Early Disease Detection using Basic Medical Data

Abstract

Early detection of diseases plays a crucial role in reducing mortality rates, improving patient outcomes, and lowering healthcare costs. With the rapid growth of artificial intelligence (AI) and machine learning (ML) techniques, data-driven diagnostic systems have emerged as powerful tools for supporting clinical decision-making. This research paper presents a quantitative AI-based framework for early disease detection using basic medical data such as age, gender, body mass index (BMI), blood pressure, blood glucose level, cholesterol, and heart rate. The proposed system utilizes supervised machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Random Forest, and Artificial Neural Networks (ANN), to classify individuals into healthy or disease-prone categories. A structured dataset consisting of 5,000 patient records was used for training and testing, with data preprocessing steps such as normalization, missing value handling, and feature selection applied to enhance model performance. Quantitative evaluation was conducted using accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curve analysis. Experimental results demonstrate that the Random Forest model achieved the highest accuracy of 92.6%, followed by ANN with 91.3%. The findings confirm that AI-based analysis of basic medical data can provide reliable early disease prediction, enabling proactive healthcare interventions and improved population health management.