Big Data-Based Healthcare Data Analysis Using Various ML and DL Techniques Identifying High-Risk Sensitive Records

In today’s era, data compression is crucial for
managing big data in the healthcare sector, reducing
dimensionality and simplifying registration complexity. The
role of artificial intelligence (AI) models in big data analysis
in healthcare is crucial for identifying patients with high-risk,
disease-impacting features that are essential for diagnosis.
Traditional machine learning (ML) models typically do not
analyse feature edges to minimise false positives. As
dimensionality increases, accuracy decreases proportionally,
affecting the accuracy of sensitive data recognition in feature
analysis. To address this issue, we propose a Multilayer
Perceptron Neural Network (MLP-NN) to identify risks in
healthcare data. Initially, Z-Score Normalisation (ZSN) is
used to preprocess the dataset. Moreover, the Support Vector
Machine (SVM) technique is employed to select the most
essential features. After that, an MLPNN is used to accurately
classify maternal health risk levels into low, medium, and
high. The proposed system enhances high-dimensional feature
reduction by leveraging auxiliary resources to improve
detection accuracy. The experimental results show that the
classification accuracy is 96.4%, which is higher than that of
other methods.