For the past few years, concepts like Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) have been slowly infiltrating the health industry, bringing with them technology and solutions that are transforming the face of healthcare. Each of these technologies is interconnected, offering something unique to the business and altering how medical professionals manage their duties and provide patient care. While AI is the most well-known of the technical terminology, DL in healthcare is a subset of AI that has disruptive potential and adds a new layer to medical technology solutions. DL is gradually becoming the leader in the field of ML, which is entering its golden age. To develop computer models, DL employs numerous layers to represent data abstractions.

Nowadays, maternal health during pregnancy is a major concern, especially in rural areas where risks are increased by a lack of medical experts and poor infrastructure. The lack of effective methods for predicting maternal health risks poses a significant challenge in maternal healthcare. Traditional approaches often fall short of accurately identifying and managing complications during pregnancy, leading to adverse outcomes. To overcome, this new technique can accurately assess medical data in order to forecast threats to maternal health. This research investigates machine learning algorithms' potential in predicting such risks using medical data. The study utilizes a carefully curated dataset called the Maternal Health Risk Dataset, encompassing parameters like age, blood pressure, glucose levels, body temperature, heart rate, and risk level. Through ensemble learning-based feature engineering, algorithms like Random Forest, XG Boost, Support Vector Classifier (SVC), Decision Tree, and Logistic Multiclass are assessed. Results show Random Forest achieving an impressive accuracy of 94.26%, indicating its potential in maternal health risk prediction, crucial for minimizing adverse outcomes and enabling timely interventions during pregnancy.

Glaucoma is a leading cause of irreversible blindness globally, with early diagnosis being crucial to preventing vision loss. Traditional diagnostic methods, including fundus photography, OCT imaging, and perimetry, often fall short in sensitivity and fail to integrate structural and functional data. This study proposes a novel multi-modal diagnostic framework that combines convolutional neural networks (CNNs), vision transformers (ViTs), and quantum-enhanced layers to improve glaucoma detection accuracy and efficiency. The framework integrates fundus images, OCT scans, and clinical biomarkers, leveraging their complementary strengths through a weighted fusion mechanism. Datasets, including the GRAPE and other public and clinical sources, were used, ensuring diverse demographic representation and supporting generalizability. The model was trained and validated using cross-entropy loss, L2 regularization, and adaptive learning strategies, achieving an accuracy of 96%, sensitivity of 94%, and an AUC of 0.97—outperforming CNN-only and ViT-only approaches. Additionally, the quantum-enhanced architecture reduced computational complexity from O(n²) to O (log n), enabling real-time deployment with a 40% reduction in FLOPs. The proposed system addresses key limitations of previous methods in terms of computational cost, data integration, and interpretability. The proposed system addresses key limitations of previous methods in terms of computational cost, data integration, and interpretability. This framework offers a scalable and clinically viable tool for early glaucoma detection, supporting personalized care and improving diagnostic workflows in ophthalmology.

Obesity is a worldwide epidemic posing a significant threat to the general population in terms of chronic metabolic diseases and low longevity. Clinical intervention and preventive health care require the early identification and proper risk prioritization. This paper compares six machine learning models, namely, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Gradient Boosting using a publicly available obesity dataset in Mexico, Peru, and Colombia (n = 2111, 17 attributes). To overcome this issue of the dominance of certain classes, the Synthetic Minority Oversampling Technique (SMOTE) was used during the pre-processing to give fair consideration of the model. Gradient Boosting was the best with highest 95.93 percent accuracy, 0.96 precision, 0.96 recall and 0.96 F1-score among all the models. SHAP (SHapley Additive Explanations) analysis was used in increasing the explain ability with each predictor indicating the most significant determinant; frequent high-calorie food consumption (FAVC, |human|) (0.18), physical activity frequency (FAF, 0.15), family history of overweight (0.12), and hydration level (0.09) significance. These quantitative insights do not only enhance interpretability, but also conform to clinical knowledge of behavioral and genetic risk factors. The suggested interpretable framework can provide a strong and clear background to the design of data-driven decision-support tools that can be used to prevent obesity, develop individual counselling, and design the policies needed by the population.