Development of a Temporal Analysis Model Augmented for Disease Progression Identification through Multiparametric Analysis
Keywords:
Progressive, Disease, VGGNet, InceptionNet, XceptionNet, Augmentation LearningAbstract
In the digital era, a hectic lifestyle and a lack of sufficient nutrition need the analysis of disease-specific aspects for the early detection of diseases in the human body. To identify the presence of heart diseases, electrocardiogram (ECG) parameters are analysed, while for identification of mental issues, electroencephalogram (EEG. These parameters' interdependency must be analysed to identify their cross-effects on different organs. For instance, improper heart functioning directly affects the normal functioning of the lungs, kidneys & liver. Continuous dysfunction of an organ indirectly causes other organs to become dysfunctional, thereby causing premature multiple-organ failure. To overcome this problem, anample diversity of algorithmic models has been defined by researchers over the years. These models need improved disease progression analysis and scalability. Inefficient system design causes clinical mistakes and reduces multi-organ analysis efficacy. This study presents an enhanced temporal analysis approach to determine illness development utilizing multiparametric analysis for a high-efficiency multi-organ analytical model. The machine learning algorithm is trained with temporal ECG, EEG, and blood records data. This data is used for building an augmented deep learning stack, which assists in evaluating the patient's current health condition and estimating progressive diseases that might affect other organs. The novelty & critical idea of the proposed model is that it utilizes an augmented combination of VGGNet-19, InceptionNet, and XceptionNet models to evaluate different diseases. Depending upon their disease-specific accuracy, these models are trained using other datasets for maximum performance. For instance, VGGNet-19 & models showcase the highest accuracy for EEG datasets. On the contrary, it has been shown that InceptionNet models exhibit superior performance when applied to electrocardiogram (ECG) signals, whereas XceptionNet is commonly employed for the classification of blood reports owing to its great efficacy in analysing one-dimensional data. These models are integrated to assess illnesses by utilizing immediate readings with a high level of efficiency. Upon collection of successively estimated readings, the model can predict disease progression with over 90% accuracy. This consistent performance across different disease types makes the system applicable for clinical usage. Furthermore, the proposed model is tested on few another dataset, and its performance and efficiency were compared with recent deep learning models, with an average 14% improvement in accuracy, 16% improvement in precision, 12% improvement in recall, and a 6% increase in computational delay was observed. While the model requires a significant training delay, the evaluation delay is moderate due to the disease-specific model design, making the system applicable for real-time clinical usage.
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