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Dr Chandrasekhar Kambhampati's 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.

An evaluation framework for mobile health education software (2015)
Presentation / Conference Contribution
Aljaber, T., Gordon, N., Kambhampati, C., & Brayshaw, M. (2015, July). An evaluation framework for mobile health education software. Presented at 2015 Science and Information Conference (SAI), London

© 2015 IEEE. Mobile applications in general, and mobile applications for health education in particular, are commonly used to support patients, health professionals and other stakeholders. A critical evaluation framework is needed to ensure the usabi... Read More about An evaluation framework for mobile health education software.

Data mining for heart failure : an investigation into the challenges in real life clinical datasets (2015)
Thesis
Kirke, L. Data mining for heart failure : an investigation into the challenges in real life clinical datasets. (Thesis). University of Hull. 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. Practical approaches to mining of clinical datasets : from frameworks to novel feature selection. (Thesis). University of Hull. 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.

CernoCAMAL : a probabilistic computational cognitive architecture (2012)
Thesis
Miri, H. CernoCAMAL : a probabilistic computational cognitive architecture. (Thesis). University of Hull. https://hull-repository.worktribe.com/output/4214117

This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its pr... Read More about CernoCAMAL : a probabilistic computational cognitive architecture.

A comparative study of missing value imputation with multiclass classification for clinical heart failure data (2012)
Presentation / Conference Contribution
Zhang, Y., Kambhampati, C., Davis, D. N., Goode, K., & Cleland, J. G. F. A comparative study of missing value imputation with multiclass classification for clinical heart failure data. Presented at 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery

Clinical data often contains missing values. Imputation is one of the best known schemes to overcome the drawbacks associated with missing values in data mining tasks. In this work, we compared several imputation methods and analyzed their performanc... Read More about A comparative study of missing value imputation with multiclass classification for clinical heart failure data.

Distributed on-line safety monitor based on safety assessment model and multi-agent system (2012)
Thesis
Dheedan, A. A. Distributed on-line safety monitor based on safety assessment model and multi-agent system. (Thesis). University of Hull. https://hull-repository.worktribe.com/output/4213377

On-line safety monitoring, i.e. the tasks of fault detection and diagnosis, alarm annunciation, and fault controlling, is essential in the operational phase of critical systems. Over the last 30 years, considerable work in this area has resulted in a... Read More about Distributed on-line safety monitor based on safety assessment model and multi-agent system.

A numerical model for Hodgkin-Huxley neural stimulus reconstruction (2011)
Journal Article
Kambhampati, C., & Sarangdhar, M. (2011). A numerical model for Hodgkin-Huxley neural stimulus reconstruction. Iaeng International Journal of Computer Science, 38(1), 89--94

The information about a neural activity is encoded in a neural response and usually the underlying stimulus that triggers the activity is unknown. This paper presents a numerical solution to reconstruct stimuli from Hodgkin-Huxley neural responses wh... Read More about A numerical model for Hodgkin-Huxley neural stimulus reconstruction.