Browsing by Author "Al Rafi, Mustahsin"

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An Early Warning System of Heart Failure Mortality With Combined Machine Learning Methods
(Institute of Advanced Engineering and Science (IAES), 2023-08-18) Sutradhar, Ananda; Al Rafi, Mustahsin; Alam, Mohammad Jahangir; Islam, Saiful
Heart failure (HF) is currently the leading cause of morbidity and mortality worldwide. Identifying the risk of mortality at the early s tages is crucial to reducing the mortality rate. However, the traditional methods for exploring the signs of mortality are difficult and time - consuming. Whereas, m achine learning (ML) methods are superior in reducing HF’s mortality rate by providing early warnings. This study presents a novel ML classifier called imperial boost - stacked (IBS) that can serve as an effective early warning system for predicting HF mortality. Initially, we performed an efficient data balancing technique named synthetic minority oversampling technique with edited nearest neighbors ( SMOTE - ENN ) to mitigate the imbalance problem. Next, two well - known feature selection techniques , the extra tree (ET) and information gain (IG), are applied to reduce the data dimensions and select the m ost significant features. Following that, the prepared feature sets are trained with our proposed IBS classifier. Simultaneously leveraging the advantages of boosting, stacking, and multiple robust methods, it significantly correlates with the intricate pa tterns of clinical data of HF patients. Finally, the robust outcomes of 92.75% accuracy over existing studies reveal that our proposed study can effectively warn the HF mortality at early stages and reduce the burden on the healthcare sector
An Intelligent Thyroid Diagnosis System Utilising Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes
(Elsevier, 2023-01-15) Sutradhar, Ananda; Al Rafi, Mustahsin; Ghosh, Pronab; Shamrat, F. M.Javed Mehedi; Moniruzzaman, Md.; Ahmed, Kawsar; Azad, AKM; Bui, Francis M.; Chen, Li; Moni, Mohammad Ali
The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including Feature Importance (FIS), Information Gain Selections (IGS), and Least Absolute Shrinkage and Selection Operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as High-Risk Factors (HRF). Second, the models, including the Three Stage Hybrid Classifier (3SHC) and the Three Stage Hybrid Artificial Neural Network (3SHANN), are used as classifiers on the training data set. Third, a Local Interpretable Model-agnostic Explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a Partial Dependence Plot (PDP). Finally, the proposed system is validated with extensive experiments where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector.
BOO-ST and CBCEC: Two Novel Hybrid Machine Learning Methods Aim To Reduce the Mortality of Heart Failure Patients
(Springer Nature Limited, 2023-12-18) Sutradhar, Ananda; Al Rafi, Mustahsin; Shamrat, F M Javed Mehedi; Ghosh, Pronab; Das, Subrata; Islam, Md Anaytul; Ahmed, Kawsar; Zhou, Xujuan; Azad, A. K. M.; Alyami, Salem A.; Moni, Mohammad Ali
Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.