Full Stack Developer & ML Engineer building AI-powered applications and scalable web platforms.

Suchitha U  • Peerlist

Developed an ensemble learning model to predict the onset of Alzheimer’s disease, achieving a high accuracy of 95.9%. Conducted a comparative analysis of individual machine learning classifiers including Logistic Regression, SVM, and Decision Trees against the ensemble approach to improve diagnostic reliability in medical applications. The project culminated in a research paper titled “Comprehensive Methodologies for Alzheimer’s Disease Prediction: A Machine Learning Approach,” which was published in the proceedings of the 2nd IEEE International Conference on IoT, Communication and Automation Technology, held at Buddha Institute of Technology, Gorakhpur, in November 2024.

Alzheimer's Prediction Using Machine Learning

Built a decentralized platform for buying, selling and collaborating on AI models, where models are stored on IPFS, metadata is recorded in mock IOTA blocks, and user authentication is managed using Firebase. The credit-based purchase system allows users to top up using Razorpay and securely access AI models.

Decentralized AI Model Marketplace Using Bloc

Developed a predictive model using ensemble learning for early Parkinson’s detection, achieving 97.4% accuracy. Applied Explainable AI techniques (LIME, SHAP) to interpret predictions and identify clinically relevant features.

Parkinson's Disease Prediction

Developed an engaging platform for 8-10th graders to learn coding through videos, games, audio lessons, and animations, with integrated progress tracking to support varied learning styles.