arXiv subjects

Syed Ibad Hasnain

Publications and source records attributed to Syed Ibad Hasnain.

3 recordsLinked to original sources

arXiv2026

Predictive Modelling of Natural Medicinal Compounds for Alzheimer disease Using Machine Learning and Cheminformatics

Alzheimer disease (AD) is a neurodegenerative disease that lacks specific treatment options. Natural drugs have displayed neuroprotective effects; however, their high-throughput discovery is challenging because of the expense of experimental testing.The study proposed a machine learning approach to identify the anti-dementia activity of natural compounds based on molecular descriptors obtained from cheminformatics. The study used a set of active and inactive compounds obtained from public databases like ChEMBL and PubChem. Various molecular descriptors, including molecular weight, lipophilicity (LogP), topological polar surface area (TPSA), and hydrogen bonding descriptors, were calculated with RDKit. Data preprocessing and feature selection were applied, followed by the development of several classification models (Random Forest, XGBoost, Support Vector Machines, Logistic Regression) and their evaluation based on accuracy, precision, recall, F1-score and ROC-AUC. The outcome suggests that ensemble techniques, such as Random Forest, delivered the best predictive accuracy and ROC-AUC values. This study also highlights that critical physicochemical descriptors in particular lipophilicity, molecular weight and polarity are important in driving neuroprotective activity as identified by feature importance analysis. The integrated machine learning approach shows the potential of combining natural product research and machine learning in early drug discovery for dementia. They provide a means of rapidly exploring large datasets and selecting candidates for experimental confirmation, thus minimising costs and time in the development of drugs for neurodegenerative diseases.

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arXiv2026

CNN-ViT Fusion with Adaptive Attention Gate for Brain Tumor MRI Classification: A Hybrid Deep Learning Model

Early detection and classifying brain tumors using Magnetic Resonance Imaging (MRI) images is highly important but difficult to extract in medical images. Convolutional Neural Networks (CNNs) are good at capturing both local texture and spatial information whereas Vision Transformers (ViTs) are good at capturing long-range global dependencies. We propose a new hybrid architecture that combines a SqueezeNet-style CNN branch with a MobileViT-style global transformer branch, through an Adaptive Attention Gate mechanism, in this paper. The gate learns dynamically per-sample, per-feature weights to weight the contribution of each branch, allowing context-sensitive merging of local and global representations. The proposed model has a test accuracy of 97.60, a precision of 97.30, a recall of 97.50, an F1-score of 97.40, and a macro-average area under the curve (AUC) of 0.9946 with a trained and evaluated on the Brain Tumor MRI Dataset (Kaggle). These scores are higher than single CNN and ViT baselines, and current competitive fusion methods, showing that dynamic feature weighting is an effective way to classify medical images.

cs.CV↗

arXiv2025

Evaluation and Implementation of Machine Learning Algorithms to Predict Early Detection of Kidney and Heart Disease in Diabetic Patients

Cardiovascular disease and chronic kidney disease are major complications of diabetes, leading to high morbidity and mortality. Early detection of these conditions is critical, yet traditional diagnostic markers often lack sensitivity in the initial stages. This study integrates conventional statistical methods with machine learning approaches to improve early diagnosis of CKD and CVD in diabetic patients. Descriptive and inferential statistics were computed in SPSS to explore associations between diseases and clinical or demographic factors. Patients were categorized into four groups: Group A both CKD and CVD, Group B CKD only, Group C CVD only, and Group D no disease. Statistical analysis revealed significant correlations: Serum Creatinine and Hypertension with CKD, and Cholesterol, Triglycerides, Myocardial Infarction, Stroke, and Hypertension with CVD. These results guided the selection of predictive features for machine learning models. Logistic Regression, Support Vector Machine, and Random Forest algorithms were implemented, with Random Forest showing the highest accuracy, particularly for CKD prediction. Ensemble models outperformed single classifiers in identifying high-risk diabetic patients. SPSS results further validated the significance of the key parameters integrated into the models. While challenges such as interpretability and class imbalance remain, this hybrid statistical machine learning framework offers a promising advancement toward early detection and risk stratification of diabetic complications compared to conventional diagnostic approaches.

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