Comparative Study of Machine Learning Techniques for Chronic Disease Prognosis

Bhola, Geetanjali; Garg, Aman; Kumari, Manisha

doi:10.1007/978-981-15-9647-6_10

Cited by 5 publications

(11 citation statements)

References 16 publications

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“…In conclusion, this study has highlighted a number of significant gaps in the literature on the use of artificial intelligence in cardiovascular diagnostics, particularly in the context of cardiovascular disease prediction 26,27,28,32 . The proposed approach has fluctuating performance in predicting myocardial infarction (MI) due to the class imbalance issue and the proposed model’s sensitivity to it.…”

Section: Discussionmentioning

confidence: 93%

“…However, the specificity (0.5261) and sensitivity (0.9232) were unbalanced for this approach 27 . Most studies 28,29,30,51,52,53,54,55 lack analysis regarding sensitivity and specificity, which are considered as crucial performance indicators for healthcare-related predictions. The studies conducted by Nasimov et al 30 and Park et al 31 analyzed the importance of input features in identifying risk factors, but the analyses lacked sufficient detail including sensitivity and interpretability analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The study of data from the Behavioural Risk Factor Surveillance System (BRFSS) included a thorough examination of forty-three selected features. These variables have been carefully chosen based on the findings of the research in similar fields 26,27,28,29,31,32,58 . This study provides a broad understanding of the many facets of behavioral risk factors and any potential effects these may have on public health.…”

Section: Feature Selectionmentioning

confidence: 99%

“…These difficulties highlight the necessity of this research in contributing to the current body of knowledge and proposing reliable methods to address the challenges identified in the literature review. Missing values and class imbalance are common issues in medical survey data, which can lead to biased conclusions and reduce the reliability of classifiers 26,27,28 . The interpretability and explainability of artificial intelligence (AI) models are critical in the medical arena since healthcare practitioners demand insights into the model’s decision-making process 32,33 .…”

Section: Introductionmentioning

confidence: 99%

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Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights

Akter,

Pias

et al. 2024

Preprint

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In the healthcare industry, many artificial intelligence (AI) models have attempted to overcome bias from class imbalances while also maintaining high results. Firstly, when utilizing a large number of unbalanced samples, current AI models and related research have failed to balance specificity and sensitivity – a problem that can undermine the reliability of medical research. Secondly, no reliable method for obtaining detailed interpretability has been put forth when addressing large numbers of input features. The present research addresses these two critical research gaps with a proposed lightweight Artificial Neural Network (ANN) model. Using 43 input features from the 2021 Behavioral Risk Factor Surveillance System (BRFSS) dataset, the proposed model outperforms prior models in producing balanced outcomes from markedly unbalanced large survey data. The efficacy of this proposed ANN model is attributed to its simplified design, which reduces processing demands, and its resilience in identifying the probability of myocardial infarction (MI). This is demonstrated by its 80% specificity and 77% sensitivity, and is substantiated by a Receiver Operating Characteristic Area Under the Curve (AUC) of 0.87. The outcomes across the scopes of each specified data domain were also separately represented, thus demonstrating the proposed model’s robust sensitivity. The interpretability of the model, as measured by Shapley values, reveals substantial correlations between myocardial infarction (MI) and its risk factors, including long-term medical conditions, socio-demographic factors, personal health habits, economic and social status, healthcare availability and affordability, as well as impairment statuses, providing valuable insights for improved cardiovascular risk assessment and personalized healthcare strategies.

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Section: Discussionmentioning

confidence: 93%

Section: Literature Reviewmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights

Akter,

Pias

et al. 2024

Preprint

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“…In [ 14 ], the authors proposed a comparative study of ML methods for the efficient diagnosis of five major diseases, including diabetes. The authors used the BRFSS dataset and trained logistic regression and RF models based on it.…”

Section: Related Workmentioning

confidence: 99%

Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods

Ullah

Saleem

Jamjoom

et al. 2022

Computational Intelligence and Neuroscience

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Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping patients reverse diabetes from the early stage to live healthy lives. Machine learning (ML) is a useful tool that can easily detect diabetes from several risk factors and, based on the findings, provide a decision-based model that can help in diagnosing the disease. This study aims to detect the risk factors of diabetes using ML methods and to provide a decision support system for medical practitioners that can help them in diagnosing diabetes. Moreover, besides various other preprocessing steps, this study has used the synthetic minority over-sampling technique integrated with the edited nearest neighbor (SMOTE-ENN) method for balancing the BRFSS dataset. The SMOTE-ENN is a more powerful method than the individual SMOTE method. Several ML methods were applied to the processed BRFSS dataset and built prediction models for detecting the risk factors that can help in diagnosing diabetes patients in the early stage. The prediction models were evaluated using various measures that show the high performance of the models. The experimental results show the reliability of the proposed models, demonstrating that k-nearest neighbor (KNN) outperformed other methods with an accuracy of 98.38%, sensitivity, specificity, and ROC/AUC score of 98%. Moreover, compared with the existing state-of-the-art methods, the results confirm the efficacy of the proposed models in terms of accuracy and other evaluation measures. The use of SMOTE-ENN is more beneficial for balancing the dataset to build more accurate prediction models. This was the main reason it was possible to achieve models more accurate than the existing ones.

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Reinforcement Learning Method for Identifying Health Issues for People with Chronic Diseases

Dhote,

Roberts,

Sridhar

2024

Lecture Notes in Electrical Engineering

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Comparative Study of Machine Learning Techniques for Chronic Disease Prognosis

Cited by 5 publications

References 16 publications

Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights

Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights

Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods

Reinforcement Learning Method for Identifying Health Issues for People with Chronic Diseases

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