Predictive Modelling and Healthcare Monitoring System Using Enhanced Variational Auto Encoder Model

• Pritam Ramesh Ahire,
• K. Ulaga Priya,

Vol. 20, No. 1, January 31, 2026
10.3837/tiis.2026.01.022, Download Paper (Free):

• Enhanced Variational Auto Encoder; Inception network (GoogleNet); ISCSO Algorithm; Obesity; Orthogonal softmax layer.

Abstract

Obesity is a growing public health issue resulting from physical inactivity and poor nutritional practices. Traditional health monitoring approaches are often subjective and inconsistent, making early-stage obesity classification challenging. Accordingly, we propose a deep learning (DL)-based framework using an Enhanced Variational Auto Encoder (EVAE) for accurate multi-stage obesity detection. The framework consists of three main phases: dataset collection, feature selection, and classification. InceptionNet (GoogLeNet) is adopted to perform feature extraction and isolate key discriminative features within the dataset to enhance input representation. The Enhanced Variational Auto Encoder (EVAE) is then used to learn latent feature representations and classify obesity stages more effectively. To further optimize model performance, the improved sand cat Swarm Optimization (ISCSO) algorithm is integrated the fine-tuning in EVAE’s key hyperparameters. The proposed method significantly outperforms existing models and achieves a high classification accuracy of 97.65%, demonstrating its reliability and effectiveness in automated obesity stage prediction and healthcare monitoring.

Statistics

Cite this article