• Mohit Dhiman
• Anjali Chaudhary
• Ved Parkash

Cardiovascular diseases (CVDs) remain the leading cause of death and illness worldwide, presenting significant diagnostic challenges due to their complex nature. Recent advancements in artificial intelligence, particularly deep learning (DL) and artificial neural networks (ANNs), offer promising solutions to enhance diagnostic accuracy. This paper presents a fine-tuned ANN framework specifically designed for CVD prediction. By leveraging pre-trained models and optimizing transfer learning techniques, the proposed framework aims to improve early detection and diagnosis of CVDs. This approach not only addresses the limitations of traditional ML and DL models, such as extensive computational resources and overfitting but also ensures clinical relevance and interpretability of the predictions. Our experimental results demonstrate the efficacy of the fine-tuned ANN model in achieving higher accuracy and lower data loss compared to traditional ANN models, thus offering a reliable tool for healthcare professionals in diagnosing and managing CVDs.

Deep Learning (DL), cardiovascular disease (CVD), Healthcare, Disease and Artificial neural networks (ANNs)

"A Fine-Tuned Artificial Neural Networks (FT-ANNs) based Framework for Cardiovascular disease (CVD) Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.i338-i346, July-2024, Available :http://www.jetir.org/papers/JETIR2407831.pdf

"A Fine-Tuned Artificial Neural Networks (FT-ANNs) based Framework for Cardiovascular disease (CVD) Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppi338-i346, July-2024, Available at : http://www.jetir.org/papers/JETIR2407831.pdf