Optimized adaptive tree seed Kalman filter for a diabetes recommendation system—bilevel performance improvement strategy for healthcare applications

Nagaraj, P.; Deepalakshmi, P.; Ijaz, Muhammad Fazal

doi:10.1016/b978-0-323-85751-2.00010-4

Cited by 35 publications

(1 citation statement)

References 15 publications

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“…Random Forest is a rapid implementation approach using the Ranger package in the R tool described by Wright and Ziegler [ 22 ], which is used to predict future illness from tabular data such as PIMA. Artificial Neural Networks based on illness prediction mechanisms, as discussed by Anifat O. et al [ 23 ] and Mantzaris D. et al [ 24 ] involve a more profound architecture that includes input and output layers alongside multiple hidden layers to process records iteratively, moving data among layers that would minimize the loss function and acquaint weights and biases of each layer. Various ensemble approaches, such as random forest and boosting, have been experimented with as alternatives to machine learning approaches for predicting future illness.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data

Srinivasu

Shafi

Krishna³

et al. 2022

Diagnostics

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The development of genomic technology for smart diagnosis and therapies for various diseases has lately been the most demanding area for computer-aided diagnostic and treatment research. Exponential breakthroughs in artificial intelligence and machine intelligence technologies could pave the way for identifying challenges afflicting the healthcare industry. Genomics is paving the way for predicting future illnesses, including cancer, Alzheimer’s disease, and diabetes. Machine learning advancements have expedited the pace of biomedical informatics research and inspired new branches of computational biology. Furthermore, knowing gene relationships has resulted in developing more accurate models that can effectively detect patterns in vast volumes of data, making classification models important in various domains. Recurrent Neural Network models have a memory that allows them to quickly remember knowledge from previous cycles and process genetic data. The present work focuses on type 2 diabetes prediction using gene sequences derived from genomic DNA fragments through automated feature selection and feature extraction procedures for matching gene patterns with training data. The suggested model was tested using tabular data to predict type 2 diabetes based on several parameters. The performance of neural networks incorporating Recurrent Neural Network (RNN) components, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) was tested in this research. The model’s efficiency is assessed using the evaluation metrics such as Sensitivity, Specificity, Accuracy, F1-Score, and Mathews Correlation Coefficient (MCC). The suggested technique predicted future illnesses with fair Accuracy. Furthermore, our research showed that the suggested model could be used in real-world scenarios and that input risk variables from an end-user Android application could be kept and evaluated on a secure remote server.

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Section: Literature Reviewmentioning

confidence: 99%