2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 16 , Issue 1 , PP: 245-262, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Gauri Sood 1 * , Neeraj Raheja 2
Doi: https://doi.org/10.54216/JISIoT.160121
Several diseases have been identified as fatal conditions affecting individuals during their middle and old ages worldwide. In recent years, chronic and pulmonary diseases have exhibited the highest mortality rates among all known conditions in this category. Machine Learning (ML) tools have efficiently studied the causes of these harmful diseases, including analyzing large databases. These databases may contain unreliable and redundant features that affect prediction accuracy and speed. Applying, the feature-based extraction and selection methods to remove inconsistent components is essential. This article implements a deep neural network (DNN) technique for diagnosis to classify different diseases. However, the DNN model faces a challenge, specifically hallucination, in accurately classifying diseases. To overcome this, a hybrid optimization DNN model has been introduced. This model is useful for recommending treatments based on the diagnosed diseases. The hybrid optimization DNN model, referred to as the Intelligent Healthcare Recommendation (IHCR) model, is designed to predict and recommend treatments for chronic and pulmonary disease patients. The research model effectively extracts features at a specific level and selects valuable features to provide accurate recommendations. This recommendation phase is followed by a statistical analysis based on probability, which evaluates patients' risk levels. Reliant on the data from the risk analysis (RA), patients are given recommendations regarding the severity and performance of the related diseases for early treatment. The proposed work has been estimated using dissimilar databases based on muti-diseases, and the outcomes appear encouraging. This research aims to develop the IHCR model for chronic and pulmonary diseases. The performance of the implemented recommendation models is evaluated using parameters like RMSE, specificity (SP), sensitivity (SN), and accuracy (Acc). The results of the recommendation model show an Acc of 96.81–97%.
Intelligent Healthcare recommender model (IHCR) , Independent Feature optimized Deep neural network (IFODNN) Classification Model , Statistical analysis-based recommender system , Grey wolf optimizer (GWO) , Deep neural network (DNN) , Independent component analysis (ICA)
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