Advancing Medical Diagnostics with Deep Learning and Data Preprocessing

Ruilin Xu; Yun Zi; Lu Dai; Haoran Yu; Mengran Zhu

doi:10.55524/ijircst.2024.12.3.24

Abstract

To address the inefficiencies and inaccuracies in analyzing large-scale medical diagnostic datasets, this paper introduces a deep learning-based method for processing auxiliary medical diagnostic data. The proposed approach involves preprocessing the medical diagnostic data through normalization and principal component analysis to extract relevant features. Subsequently, a neural network utilizing a multilayer perceptron is employed to analyze the preprocessed data, facilitating diagnostic classification. It also provides intelligent support for medical professionals. The method was implemented and tested using the Python programming environment. Results indicate that the proposed approach achieves better performance than other comparative methods and demonstrates significant practical application potential.

Keywords

Deep Learning, Medical Auxiliary Diagnosis, Data Analysis, Principal Component Analysis, Multilayer Perceptron Neural Network.

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Cites this article as

R. Xu, Y. Zi, L. Dai, H. Yu, M. Zhu, "Advancing Medical Diagnostics with Deep Learning and Data Preprocessing", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-12, no.3, pp.143-147, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.3.24

Corresponding Author

Ruilin Xu

The University of Chicago, Chicago, USA

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