Milestone Transactions on Medical Technometrics

Personalized Heart Disease Prediction Using Data-Driven Machine Learning Approaches

2026-01-16T12:56:26+00:00

Cardiovascular diseases (CVDs) are persistently projected as one of the current major health concerns across the globe, thereby emphasizing the importance of an accurate and personalized prediction model. The typical predictive models currently used for health-related diagnostics are mostly based on general models and clinical screening, and in some cases, they are incapable of examining nonlinear interconnections among specific patient risk factors. To address this shortcoming, we propose a machine learning model for personalized heart disease prediction. Multiple supervised machine learning models, namely Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Random Forest (RF), are developed and compared by using the popular UCI Heart Disease dataset. Extensive preprocessing and normalization techniques are used in this study to improve prediction accuracy. Our proposed models show the performance evaluation processes using ROC-AUC, learning curves, and calibration analyses, which justify the accuracy, applicability, and interpretability of the models. The results of this study show that Random Forest's cardiovascular classification, with an accuracy of 98.01%, a Precision of 97.90%, a Recall of 97.99%, and an F1-score of 98.00%, outperformed all other machine learning models.

Posture+: An AI-Driven Hybrid Care Platform for Optimizing Musculoskeletal Health

2026-02-05T15:17:33+00:00

Musculoskeletal (MSK) disorders impose a significant clinical and economic burden, making them one of the largest drivers of global healthcare costs. Posture+ addresses this challenge through a patient-centric digital platform that combines personalized therapeutic exercises with expert human guidance. Its core innovation, the proprietary TrueMotion system, uses advanced AI and computer vision to deliver real-time, high-fidelity corrective feedback, ensuring clinical accuracy and improving adherence through behavioral science principles. The PT-Augmentation Model enables clinicians to focus on complex cases while AI automates routine tasks, creating a scalable, efficient care ecosystem. Looking ahead, Posture+ is poised to expand into the broader Digital Chronic Care Management (DCCM) market by integrating solutions for metabolicdisorders, behavioral health, and other co-morbidities. Success in this next phase will rely on predictive AI development, strengthened regulatory compliance, and deeper payer-led partnerships that support Value-Based Care (VBC). With these advancements, Posture+ is positioned to become a global leader in delivering high-quality, cost-effective hybrid healthcare.

Secure and Transparent Blockchain-Enabled System for Pharmaceutical Drug Authentication

2026-02-10T13:40:49+00:00

Counterfeit drugs are an extremely critical issue that affects global public health, economic security, and trust in government regulations. The existing drug management systems are mainly centralized and opaque, with little data integrity, and are easily susceptible to data manipulation. We present a secure, transparent, blockchain-enabled framework for pharmaceutical drug authentication and end-to-end supply chain verification. The proposed system takes advantage of the decentralized, immutable, and tamper-resistant nature of blockchain technology in recording and validating critical supply chain activities, which include manufacturer registration, distributor verification, pharmacy validation, and administrative approval. A unique QR code is generated for each drug product, which serves as the drug product's digital identity and helps ensure traceability and authenticity verification for the end consumer. The proposed system has been implemented using the Django web application framework, which has an integrated custom blockchain architecture that utilizes SHA-256 to ensure data integrity and prevent unauthorized tampering with records. Based on the experimental results, 100% integrity validation of the blockchain technology has been achieved, along with accurate detection of any attempted tampering, efficient transaction processing, and verification using the QR code technology with minimal response time. It has been observed that the proposed framework is effective for ensuring the traceability of the drug product, building trust among the various stakeholders, and fighting the menace of counterfeit medicines without the need for any central authority.