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Nagaland University, Kohima Campus, Meriema

Nagaland University

Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD. SEFRON explores the advantages of Spiking Neural Network (SNN) which is suitable for neuromorphic devices. To evaluate the performance of SEFRON, 2 publicly available standard datasets, namely (1) UCI: Oxford Parkinson's Disease Detection Dataset and (2) UCI: Parkinson Dataset with replicated acoustic features are used. The performance is compared with other well-known neural network models: Multilayer Perceptron Neural Network (MLP-NN), Radial Basis Function Neural Network (RBF-NN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). The experimental results demonstrate that the SEFRON classifier achieves a maximum accuracy of 100% and an average accuracy of 99.49% on dataset 1. For dataset 2, it attains a peak accuracy of 94% and an average accuracy of 91.94%, outperforming the other classifiers in both cases. From the performance, it is proved that the presented model can help to develop a robust automated PD detection device that can assist the physicians to diagnose the disease at its early stage.

A robust Parkinson's disease detection model based on time-varying synaptic efficacy function in spiking neural network.

Diabetes remains a major public health challenge, contributing to complications such as kidney disease, cardiovascular disorders, and diabetic retinopathy. Early detection is essential for timely intervention, yet prediction from structured biomedical data is often hindered by limited sample size and feature diversity. This study investigates a deep learning framework that combines tabular-to-image transformation, pre-trained Convolutional Neural Networks, and Long Short-Term Memory (LSTM) networks to enhance diabetes prediction. Using the Pima Indians Diabetes Dataset, numerical features were transformed into 2D image representations based on correlation patterns and feature importance scores. Conditional Generative Adversarial Networks generated additional synthetic samples for training. Feature extraction was performed with DenseNet201, ResNet152, Xception, and EfficientNetB4, followed by classification using LSTM networks optimised via Bayesian search. In five-fold cross-validation, the deep learning pipeline achieved 94% accuracy and 98% AUC on the augmented PIMA dataset, showing improved performance compared to commonly reported benchmarks; however, these results may partially reflect the influence of synthetic data. When evaluated on the Frankfurt Diabetes Dataset, the model exhibited comparable performance, although the limited number of samples indicates that additional studies are required to firmly establish its generalizability. The proposed framework demonstrates promising performance for diabetes prediction from structured data. While the results suggest potential applicability to broader biomedical classification tasks, further validation on large, demographically diverse, and multi-institutional datasets is essential before considering any clinical translation.

ArticleTotal : 3	Year
Publication title	2026
Publication title	2026
Publication title	2026

Article	Year
A robust Parkinson's disease detection model based on time-varying synaptic efficacy function in spiking neural network. BMC neurology\| PubMed ID: 39734199	2024
Enhanced diabetes prediction using pre-trained CNNs, LSTM, and conditional GAN on transformed numerical data. Scientific reports\| PubMed ID: 41667617	2026

Sujata Dash

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