Computational Methods for Predicting Chronic Disease in Healthcare Communities

Induja, S.N; Raji, C. G.

doi:10.1109/icondsc.2019.8817044

Cited by 17 publications

(18 citation statements)

References 12 publications

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“…3) Decision Tree (DT) Classi er: Decision tree is the most leading and conventional tool for grouping and prediction 12]. A Decision tree is a owchart like tree structure, where each inside hub shows a check on the data, each branch represents holds a class mark [12]. A tree can be learned by part the source set into subsets dependent on a quality esteem check.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The recursion is completed when the subset at a hub all has a related valuation of the objective data, or while part never again rises the rate of the predictions. The progress of decision tree model does not involve any space statistics or attribute setting, and along these lines is appropriate for experimental learning exposure [12]. Decision trees can perform with high dimensional data and has good precision.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Decision trees can perform with high dimensional data and has good precision. Decision tree approval is an average inductive method to perform with learns information on instruction [12].…”

Section: Proposed Methodologymentioning

confidence: 99%

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Artificial Neural Networks Model in the Prediction of Pancreas Transplantation Recipient Survival

Raji¹,

S.N²

2022

Preprint

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The pancreas transplantation is always a risky procedure, so that the number of transplantation suggesting by doctors are less. Also doctors have no method to predict the survival of patient after pancreas transplantation. So it is necessary to have a system to predict the survival of patients after pancreas transplantation. Mostly Type 1 diabetes are treated through pancreas transplantation. Yet, it is commonly held for those with genuine diabetes difficulties, on the grounds that the reactions of a pancreas transplant are huge. Consequently the requirement for this investigation is basic. Pancreas transplantation is constantly dangerous in light of it’s eventual outcomes, so the proposed framework anticipating the odds of survival of patients by breaking down their wellbeing conditions. This investigation will be useful to specialists for understanding careful results and expands the believability of specialists. From our research, we can see that the chosen model have given greater survival rate. Pancreas transplantation is definitely not a standard procedure yet a seldom happening procedure, since it is an exceptionally unsafe technique and has numerous eventual outcomes. Need an exact model to foresee the survival after transplantation. We proposed three models which are suitable for pancreas survival prediction. From that, with Multilayer Perceptron we obtained 99.5% accuracy in survival prediction. The result was compared with existing research work to ensure that it is the only accurate method now existing to predict the patients survival in pancreas transplantation.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

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Artificial Neural Networks Model in the Prediction of Pancreas Transplantation Recipient Survival

Raji¹,

S.N²

2022

Preprint

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“…Along with the objective variable, exploratory variables such as age, gender, and analysis period are also included, since the aim of this paper is proper management of the budget for medical care [33]. A review that highlights the applications of machine learning techniques in various medical practices such as predicting, diagnosing, and prognosis of diseases such as multiple sclerosis, autoimmune chronic kidney disease, autoimmune rheumatic disease, and inflammatory bowel disease and for the selection of treatments and stratification of patients; drug development; drug repurposing; target interpretation; and validation has been given in [34,35]. is paper also provides a detailed description of the challenges faced by the machine learning approaches such as the need for quality data in preparation of robust models, external model validation using the independent data set, difficulties faced during implementation of a model, and ethical concerns.…”

Section: Related Workmentioning

confidence: 99%

“…Also, this disease can commonly be caused due to ageing. Chronic diseases include cardiovascular disease, cancer, arthritis, diabetes, obesity, epilepsy and seizures, and problems in oral health [35].…”

Section: Chronic Disease According To Us National Center Formentioning

confidence: 99%

Identification and Prediction of Chronic Diseases Using Machine Learning Approach

Rayan

2022

Journal of Healthcare Engineering

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Nowadays, humans face various diseases due to the current environmental condition and their living habits. The identification and prediction of such diseases at their earlier stages are much important, so as to prevent the extremity of it. It is difficult for doctors to manually identify the diseases accurately most of the time. The goal of this paper is to identify and predict the patients with more common chronic illnesses. This could be achieved by using a cutting-edge machine learning technique to ensure that this categorization reliably identifies persons with chronic diseases. The prediction of diseases is also a challenging task. Hence, data mining plays a critical role in disease prediction. The proposed system offers a broad disease prognosis based on patient’s symptoms by using the machine learning algorithms such as convolutional neural network (CNN) for automatic feature extraction and disease prediction and K-nearest neighbor (KNN) for distance calculation to find the exact match in the data set and the final disease prediction outcome. A collection of disease symptoms has been performed for the preparation of the data set along with the person’s living habits, and details related to doctor consultations are taken into account in this general disease prediction. Finally, a comparative study of the proposed system with various algorithms such as Naïve Bayes, decision tree, and logistic regression has been demonstrated in this paper.

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