Milestone Transactions on Medical Technometrics

Machine Learning Hybrid Models for Early Cervical Cancer Detection – A Comparative Study

2025-05-30T15:40:15+00:00

Cervical cancer arises as a result of the uncontrolled growth of abnormal cells in the cervix, usually caused by chronic infection with high-risk types of Human PapillomaVirus (HPV). Early detection and prevention can be facilitated through regular screening and HPV vaccination. This study proposes an enhanced ML Hybrid Model focused on the early spotting of Cervical Cancer using the best available Machine Learning techniques. To solve a primary challenge in diagnosing accuracy and precision in Cervical Cancer, the model utilizes AdaBoost, XGBoost, Stacking Classifiers, and Logistic Regression. The Hybrid Model uses ensemble methods such as AdaBoost and XGBoost, which improves productivity by properly integrating poor learners. Stacking Classifiers increases accuracy even more by incorporating the predictions of several models while Logistic Regression adds interpretability as well as reliability to the results. Collectively, these approaches form a model that produces low false positive rates alongside low false negative rates for early detection of the disease. This study focuses on the effects that Machine Learning can have in treating advanced stages of healthcare issues especially relating to early detection of Cervical cancer. The combination of sophisticated computational methods with clinical data sets presents an effective, globally relevant solution to urgent health concerns.

Stress Monitoring With Heart Rate Variability Using Deep Learning

2025-04-23T07:15:51+00:00

Prolonged stress can lead to mental health issues like anxiety and sleep disorders. Heart Rate Variability (HRV) serves as a key physiological marker for stress detection. Unlike heart rate, HRV measures the variation in time intervals between heartbeats (RR intervals). This study proposes a CNN-based model for classifying stress into no stress, interruption stress, and time pressure stress using HRV features. Evaluated on the SWELL-KW dataset, the model achieves 99.9% accuracy, outperforming existing methods. Feature extraction techniques, such as ANOVA, further validate the significance of HRV features in stress detection.

Securing Patient Data With Blockchain Enabled Federated Learning For Medical Diagnostics

2025-04-23T09:24:14+00:00

The proposed framework introduces a groundbreaking solution in healthcare by integrating Federated Learning (FL) with blockchain technology for diagnosing lung diseases. Traditional machine learning models often rely on centralized data collection, raising concerns about patient data privacy and potential misuse of sensitive information. FL tackles this issue by allowing hospitals to collaborate in training machine learning models without sharing raw patient data. Each hospital processes its local data independently, sending encrypted model updates instead, ensuring that data privacy is preserved while fostering collective innovation. To further strengthen this approach, blockchain technology is employed to securely encrypt and immutably store the shared model updates, creating a transparent and tamper-proof system. This combination not only addresses privacy concerns but also builds trust and accountability among participating hospitals. Significantly, the framework empowers smaller hospitals and under-resourced medical centers to contribute to and benefit from advanced diagnostic capabilities. By pooling resources through FL and ensuring equitable access via blockchain, it reduces disparities between healthcare providers, enabling improved diagnostics in distant or underserved areas. To the best of your knowledge, this represents the first practical implementation of blockchain-empowered FL on such diverse medical data, making a substantial contribution to the integration of artificial intelligence, blockchain, and healthcare. With its potential to revolutionize collaborative diagnostics while prioritizing privacy and security, this framework sets a new standard for technological innovation in medicine

Early Detection of Autism Spectrum Disorder using Transfer Learning on Brain Imaging Data

2025-04-23T09:53:42+00:00

This study focuses on classifying and representing learning tasks using powerful deep learning models, including Convolutional Neural Networks (CNN) and Transfer Learning algorithms. The analysis utilizes data from the Autism Brain Imaging Data Exchange (ABIDE I and ABIDE II) datasets. It explores the application of deep learning techniques to enhance the detection of Autism Spectrum Disorder (ASD). Functional magnetic resonance imaging (fMRI) data has shown potential in identifying brain irregularities associated with ASD. We utilize Convolutional Neural Networks (CNNs) combined with transfer learning to analyse fMRI data from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Our optimized CNN model achieves an accuracy of 81%, surpassing conventional classification models. This research establishes deep learning as a promising tool for ASD diagnosis, evaluated based on accuracy, precision, and recall