Human Critical Organ Disease Prediction Using Deep Learning
Abhishek Balwant Bhagwat, Amita Jajoo, Laxmikant Maruti Athare, Kunal Santosh Narawade, Sahil Shashikant Matondkar, Anup Kalyanshetti
Deep Leaning, CNN, Image Processing, Disease Prediction, Model integration
Deep learning techniques have transformed medical science by enabling the early prediction of serious organ disorders in humans, such as those affecting the heart, brain, and kidneys. This paper presents a thorough investigation into disease prediction using Convolutional Neural Networks (CNNs) alongside machine learning algorithms. Key challenges including model overfitting, interpretability limitations, imbalanced datasets, and data scarcity are examined. The study underscores the importance of transparent and interpretable models in medical decision-making, advocating for the development of explainable AI methods tailored to healthcare needs. The research demonstrates promising performance in accuracy, sensitivity, and specificity, showcasing the potential of deep learning in enhancing the diagnosis and prognosis of critical organ diseases. By addressing these challenges, the proposed framework offers a robust and effective approach to disease prediction, contributing to the advancement of medical diagnostics. This approach holds the promise of revolutionizing healthcare by facilitating early intervention and personalized treatment strategies for patients with critical organ diseases.
Article Details
Unique Paper ID: 164871

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 2684 - 2688
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