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Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure (2022)
Journal Article
Kazmi, S., Kambhampati, C., Cleland, J., Cuthbert, J., Kazmi, K. S., Pellicori, P., …Clark, A. L. (2022). Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure. ESC Heart Failure, https://doi.org/10.1002/ehf2.14028

Aims: Risk changes with the progression of disease and the impact of treatment. We developed a dynamic risk stratification Markov chain model using artificial intelligence in patients with chronic heart failure (CHF). Methods and results: We describe... Read More about Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure.

Locally fitting hyperplanes to high-dimensional data (2022)
Journal Article
Hou, M., & Kambhampati, C. (2022). Locally fitting hyperplanes to high-dimensional data. Neural Computing and Applications, 34(11), 8885-8896. https://doi.org/10.1007/s00521-022-06909-y

Problems such as data compression, pattern recognition and artificial intelligence often deal with a large data sample as observations of an unknown object. An effective method is proposed to fit hyperplanes to data points in each hypercubic subregio... Read More about Locally fitting hyperplanes to high-dimensional data.

Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection (2022)
Journal Article
Gordon, N., Kambhampati, C., & Alabad, A. (2022). Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection. AI, Computer Science and Robotics Technology, 1, 1-21. https://doi.org/10.5772/acrt.01

This article provides an optimisation method using a Genetic Algorithm approach to apply feature selection techniques for large data sets to improve accuracy. This is achieved through improved classification, a reduced number of features, and further... Read More about Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection.

Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease (2020)
Journal Article
Alabed, A., Kambhampati, C., & Gordon, N. (in press). Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease. Advances in Intelligent Systems and Computing, 1229 AISC, 531-543. https://doi.org/10.1007/978-3-030-52246-9_38

A great wealth of information is hidden in clinical datasets, which could be analyzed to support decision-making processes or to better diagnose patients. Feature selection is one of the data pre-processing that selects a set of input features by rem... Read More about Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease.

Ionic Imbalances and Coupling in Synchronization of Responses in Neurons (2019)
Journal Article
Sadegh-Zadeh, S., Kambhampati, C., & Davis, D. N. (2019). Ionic Imbalances and Coupling in Synchronization of Responses in Neurons. J — Multidisciplinary Scientific Journal, 2(1), 17-40. https://doi.org/10.3390/j2010003

Most neurodegenerative diseases (NDD) are a result of changes in the chemical composition of neurons. For example, Alzheimer's disease (AD) is the product of Aβ peptide deposition which results in changes in the ion concentration. These changes in io... Read More about Ionic Imbalances and Coupling in Synchronization of Responses in Neurons.

Computational methods toward early detection of neuronal deterioration (2019)
Thesis
Sadegh-Zadeh, S. (2019). Computational methods toward early detection of neuronal deterioration. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4221749

In today's world, because of developments in medical sciences, people are living longer, particularly in the advanced countries. This increasing of the lifespan has caused the prevalence of age-related diseases like Alzheimer’s and dementia. Research... Read More about Computational methods toward early detection of neuronal deterioration.

Autoencoder for clinical data analysis and classification : data imputation, dimensional reduction, and pattern recognition (2017)
Thesis
Al Khaldy, M. (2017). Autoencoder for clinical data analysis and classification : data imputation, dimensional reduction, and pattern recognition. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4224219

Over the last decade, research has focused on machine learning and data mining to develop frameworks that can improve data analysis and output performance; to build accurate decision support systems that benefit from real-life datasets. This leads to... Read More about Autoencoder for clinical data analysis and classification : data imputation, dimensional reduction, and pattern recognition.

Data mining for heart failure : an investigation into the challenges in real life clinical datasets (2015)
Thesis
Kirke, L. (2015). Data mining for heart failure : an investigation into the challenges in real life clinical datasets. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4218159

Clinical data presents a number of challenges including missing data, class imbalance, high dimensionality and non-normal distribution. A motivation for this research is to investigate and analyse the manner in which the challenges affect the perform... Read More about Data mining for heart failure : an investigation into the challenges in real life clinical datasets.

Practical approaches to mining of clinical datasets : from frameworks to novel feature selection (2014)
Thesis
Poolsawad, N. (2014). Practical approaches to mining of clinical datasets : from frameworks to novel feature selection. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4215841

Research has investigated clinical data that have embedded within them numerous complexities and uncertainties in the form of missing values, class imbalances and high dimensionality. The research in this thesis was motivated by these challenges to m... Read More about Practical approaches to mining of clinical datasets : from frameworks to novel feature selection.

Issues in the mining of heart failure datasets (2014)
Journal Article
Poolsawad, N., Moore, L., Kambhampati, C., & Cleland, J. G. (2014). Issues in the mining of heart failure datasets. International Journal of Automation and Computing, 11(2), 162-179. https://doi.org/10.1007/s11633-014-0778-5

This paper investigates the characteristics of a clinical dataset using a combination of feature selection and classification methods to handle missing values and understand the underlying statistical characteristics of a typical clinical dataset. Ty... Read More about Issues in the mining of heart failure datasets.