Efficient Chronical Disease Prediction Using Improved Convolutional Neural Network Model
Keywords:
Chronic Disease, CNN, optimizer, deep learning (DL), PythonAbstract
Accurate analysis of medical data aids with initial illness identification, community services, as well as patient treatment in the healthcare fields. However, when the medical data quality is deficit, the analysis's accuracy suffers. The study's objective is to create an artificial intelligence system for chronic diseases detection that is deep learning-based. The experiment is conducted using various healthcare data that was obtained from Kaggle. Pre-processing is used to fill in the gaps in the data in directive to get around the challenge of imperfect data. This study suggests a novel multimodal disease risk prediction method grounded on convolutional neural networks (CNNs) that uses both structured and unstructured input. The Improved Convolutional Neural Network with Nadam Optimizer (I-NCNN) is the algorithm that is put forth in this paper. As far, there is only some research on multimodal disorders. The implementation takes advantage of the Python Jupyter environment. Performance measures, including precision, accuracy, F1 score, also recall, are make use of to assess the efficiency of the proposed I-NCNN model. The prediction accuracy of the proposed algorithm is 96% when likened to various other existing
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