The ClinicalML is an artificial intelligence-based healthcare system designed to assist in early disease prediction and personalized treatment support. It uses advanced machine learning algorithms to analyze patient data and identify health conditions accurately. The system focuses on common diseases such as diabetes, hypertension, and cardiovascular disorders using important parameters like age, BMI, blood pressure, and glucose levels. ClinicalML follows a two-stage approach in which Random Forest, Gradient Boosting, and Multi-Layer Perceptron (MLP) models are combined using a soft voting ensemble method for accurate disease classification. Based on the predicted disease, the system provides personalized diet plans and medication schedules. The complete system runs within a web browser without requiring backend servers or cloud infrastructure, ensuring faster processing, better privacy, and easy accessibility. ClinicalML also includes charts and graphs to help users and doctors understand the results and make informed healthcare decisions quickly.
Keywords: AI in Healthcare, Disease Prediction, Diet & Drug Recommendation, Random Forest, Gradient Boosting, Multi-Layer Perceptron, Soft Voting Ensemble
G. Vamsi, K. Devendra· International Scientific Jou...· 0 citations
Hypertension, commonly known as high blood pressure, is a major risk factor for cardiovascular diseases and premature mortality worldwide. Early detection and prevention are critical in reducing its health impact. This study explores the application of machine learning (ML) techniques to predict the likelihood of hypertension in individuals using clinical and demographic data. A variety of supervised learning algorithms, including Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting, were evaluated for their predictive performance [1]. The dataset was preprocessed through feature selection, normalization, and handling of missing values to improve model accuracy.[2] Performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC were used to assess the models [4]. The results demonstrate that ML models can effectively identify individuals at high risk of hypertension, offering a valuable tool for early intervention and personalized healthcare [5]. This approach underscores the potential of artificial intelligence in supporting public health efforts and enhancing clinical decision-making.
Key words: Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting.
G. Vamsi, K. Bhargavi· International Scientific Jou...· 0 citations
The resulting prototype confirms that a cloud-hosted multimodal LLM, when combined with a minimal and well-structured web stack, can serve as a practical foundation for next-generation digital assistants suitable for customer support, education, and personal productivity applications.
G. Vamsi, Vinay Kumar Male· International Scientific Jou...· 0 citations