Funding Alzheimer’s drug development: Blockchain-based crowdfunding with risk assessment models for financial projections

Alzheimer’s drug research is beset with continuous funding challenges due to the high rate of clinical failure and long development periods. The complexity and danger of biomedical projects are too much for traditional crowdfunding to handle. Using a C5.0 decision tree to categorize drug development stages into risk levels based on clinical data such as brain biomarkers and mini-mental state examination (MMSE) scores, this study presents a blockchain-based, risk-aware fundraising platform. Smart contract-driven fund releases are guided by risk thresholds, which lowers investor exposure. XGBoost and Random Forest models trained on initial coin offering (ICO) and Kickstarter data are used to forecast financial results. The system, which includes trust scoring, automated refunds, and tokenized incentives, has a 78% financing success rate. Improved openness, investor confidence, and fraud prevention are demonstrated by simulations, which promote safe, scalable investment in early-stage pharmaceutical R&D. The system being put forward here sets a new benchmark in safe, data-oriented, and intelligent crowdfunding for advanced clinical research facilities.