Chronic Diseases Diagnosis using Machine Learning

Ganiger, Shweta; Rajashekharaiah, K.M.M.

doi:10.1109/iccsdet.2018.8821235

Cited by 20 publications

(2 citation statements)

References 8 publications

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“…Little research is performed in identifying the accuracy and predictive power for developing a machine learning model with only information from lab examination results for the diagnosis of diseases. And, for performance enhancement, ensemble machine learning and deep learning model can be used [21,22]. In the healthcare domain, artificial intelligence (AI) plays a major role in automating the roles involved in disease diagnosis and treatment suggestions and also schedules perfect timing by the medical practitioners to perform various obligations that cannot be automated [23].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

Identification and Prediction of Chronic Diseases Using Machine Learning Approach

Rayan

2022

Journal of Healthcare Engineering

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“…Decision-making of a human may be productive, but it is not up to the mark when the amount of data to be classified is massive and should be avoided in a sensitive real-time area like the clinical domain. Decision-making based on inconsistent clinical data records is a very common error observed during manual diagnosis [ 3 ]. Thus, it is better suited to enhance the usage of predictive learning models [ 4 ] in the medical field by implementing it as an intelligent problem-solving approach [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

A Decisive Metaheuristic Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risk Assessment

Mishra

Thakkar

Singh

et al. 2022

Computational Intelligence and Neuroscience

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Advanced predictive analytics coupled with an effective attribute selection method plays a pivotal role in the precise assessment of chronic disorder risks in patients. Traditional attribute selection approaches suffer from premature convergence, high complexity, and computational cost. On the contrary, heuristic-based optimization to supervised methods minimizes the computational cost by eliminating outlier attributes. In this study, a novel buffer-enabled heuristic, a memory-based metaheuristic attribute selection (MMAS) model, is proposed, which performs a local neighborhood search for optimizing chronic disorders data. It is further filtered with unsupervised K-means clustering to remove outliers. The resultant data are input to the Naive Bayes classifier to determine chronic disease risks’ presence. Heart disease, breast cancer, diabetes, and hepatitis are the datasets used in the research. Upon implementation of the model, a mean accuracy of 94.5% using MMAS was recorded and it dropped to 93.5% if clustering was not used. The average precision, recall, and F-score metric computed were 96.05%, 94.07%, and 95.06%, respectively. The model also has a least latency of 0.8 sec. Thus, it is demonstrated that chronic disease diagnosis can be significantly improved by heuristic-based attribute selection coupled with clustering followed by classification. It can be used to develop a decision support system to assist medical experts in the effective analysis of chronic diseases in a cost-effective manner.

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An Enhanced Learning Model Based on an Improved Random Forest Classifier and an Integrated Attribute Selector for Healthcare Datasets

Rajeashwari,

Arunesh

2024

Communications in Computer and Information Science

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Chronic Diseases Diagnosis using Machine Learning

Cited by 20 publications

References 8 publications

Identification and Prediction of Chronic Diseases Using Machine Learning Approach

Identification and Prediction of Chronic Diseases Using Machine Learning Approach

A Decisive Metaheuristic Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risk Assessment

An Enhanced Learning Model Based on an Improved Random Forest Classifier and an Integrated Attribute Selector for Healthcare Datasets

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