eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-02-20 18 March Spl Edition 161 177 10.13005/bpj/3079 63907 article Janmejay Pant 1 Hitesh Kumar Pant 2 Vinay Kumar Pant 3 Vikas Bhatt 4 Devendra Singh 1 Kapil Joshi 5 Department of Computer Science and Engineering Graphic Era Hill University Bhimtal Campus, India Department of Management Studies, Kumaun University, Bhimtal, India Department of Computer Science and Engineering Moradabad Institute of Technology Moradabad, India Faculty of Pharmaceutical Sciences Amrapali University Haldwani, India Department of Computer Science and Engineering Graphic Era Hill University Bhimtal Campus, India Department of Computer Science and Engineering Uttaranchal University, Dehradun India Parkinson's disease (PD) is a chronic, non-fatal, and well-known progressive neurological disorder, the symptoms of which often overlap with other diseases. Effective treatment of diseases also requires accurate and early diagnosis, a way that patients can lead healthy and productive lives. The main PD signs are resting tremors, muscular rigidity, akinesia, postural instability, and non-motor signs. Clinician-filled dynamics have traditionally been an essential approach to monitoring and evaluating Parkinson's Disease (PD) using checklists. Accurate and timely diagnosis of Parkinson's disease (PD), a chronic and progressive neurological ailment, can be difficult due to its symptoms overlapping with those of other disorders. Effective therapy and improvement in the quality of life for patients depend on early and accurate detection. To improve classification performance, this study investigates transfer learning, which uses pre-trained models to extract features from massive datasets. Transfer learning improves generalization and permits domain adaptation, especially for small or resource-constrained datasets, while lowering training time, resource needs, and overfitting concerns. This work aims to design and assess a general transfer learning paradigm for the reliable prognosis of Parkinson’s disease based on DaTscan images that consider feature extraction and the performance of a variety of ML algorithms. This work aims to explore the use of transfer learning with pre-trained deep learning models to extract features from DaTscan images in order to improve classification accuracy. The sample of this study is made up of 594 DaTscan images from 68 participants, 43 with PD and 26 healthy. Out of the four algorithms employed; the Random Forest, Neural Network, Logistic Regression, and Gradient Boosting models, transfer learning-based features were applied.  Four indices of accuracy, namely Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall and Matthews Correlation Coefficient (MCC) were used to evaluate four machine learning models on a PD classification task such as Random Forest, Neural Network, Logistic Regression, and Gradient Boosting. Neural networks outperformed the other models, showing robustness and reliability with an AUC of 0.996, CA of 0.973, and MCC of 0.946. Gradient Boosting performed competitively, coming in second with an AUC of 0.995 and MCC of 0.925. Random Forest performed the worst, with an AUC of 0.986 and an MCC of 0.905, whereas Logistic Regression had an AUC of 0.991 and an MCC of 0.926. These results demonstrate how well neural networks perform high-precision tasks and point to gradient boosting as a more computationally effective option. https://biomedpharmajournal.org/vol18marchspledition/deep-learning-based-feature-extraction-and-machine-learning-models-for-parkinsons-disease-prediction-using-datscan-image/ Deep Learning Feature Extraction Neurological disorder Parkinson's disease Transfer Learning