Outputs (48)

Progression of risk in heart failure using dynamic risk modelling (2024)
Thesis
Kazmi, S. (2024). Progression of risk in heart failure using dynamic risk modelling. (Thesis). University of Hull. https://hull-repository.worktribe.com/output/5086141

Heart failure (HF) is a prevalent condition affecting a significant number of individuals in the UK, leading to substantial healthcare utilisation and adverse outcomes. Despite advancements in treatment and management, the prognosis for hospitalised... Read More about Progression of risk in heart failure using dynamic risk modelling.

Disease progression in chronic heart failure is linear: Insights from multistate modelling (2024)
Journal Article
Kazmi, S., Kambhampati, C., Rigby, A. S., Cleland, J. G. F., Kazmi, K. S., Cuthbert, J., Pellicori, P., & Clark, A. L. (online). Disease progression in chronic heart failure is linear: Insights from multistate modelling. European journal of heart failure, https://doi.org/10.1002/ejhf.3400

Aims: Understanding the pattern of disease progression in chronic heart failure (HF) may inform patient care and healthcare system design. We used a four-state Markov model to describe the disease trajectory of patients with HF. Methods and results:... Read More about Disease progression in chronic heart failure is linear: Insights from multistate modelling.

A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy (2024)
Journal Article
Xue, Y., Kambhampati, C., Cheng, Y., Mishra, N., Wulandhari, N., & Deutz, P. (2024). A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy. International Journal of Computational Intelligence Systems, 17(1), Article 8. https://doi.org/10.1007/s44196-023-00375-7

The mass production of plastic waste has caused an urgent worldwide public health crisis. Although government policies and industrial innovation are the driving forces to meet this challenge, trying to understand public attitudes may improve the effi... Read More about A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy.

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., Rigby, A. S., & 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)
Presentation / Conference Contribution
Alabed, A., Kambhampati, C., & Gordon, N. Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease. Presented at Computing 2020, London

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.-A., 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.-A. Computational methods toward early detection of neuronal deterioration. (Thesis). University of Hull. 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. Autoencoder for clinical data analysis and classification : data imputation, dimensional reduction, and pattern recognition. (Thesis). University of Hull. 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.