Browsing by Author "Alam, Md. Mahabur"

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A Decision Support System for Early Prediction of Brain Stroke Disease in Bangladesh
(Daffodil International University, 2019-04) Alam, Md. Mahabur; Hasan, Md. Mehadi
Brain Stroke is a Neurological disease that occurs when the blood supply to brain is interrupted or reduce, depriving brain tissue of oxygen and nurturance. This can lead to brain damage to possibly death. Brain Stroke are a medical emergency and prompt treatment is essential because the sooner a person receives treatment stroke, the less damage is likely to happen. Brain Stroke is the second leading cause of death in worldwide and third in Bangladesh. Information gain from health data may lead to innovative solution of better treatment plan for patients. In order to gain knowledge intelligently from brain stroke data, some machine learning technique and utilized to process data and generated data model that can be used to predict brain stroke disease or extract valuable information about brain stroke disease. In this study a data mining model has been build using a few machine learning algorithm, which can find out the important features of brain stroke as well as efficiently predict brain stroke.
An Efficient Modified Bagging Method for Early Prediction of Brain Stroke
(Scopus, 2019-07-12) Alam, Md. Mahabur; Hasan, Md. Mehadi; Hasan, Md. Zahid
Brain stroke become a serious cardiovascular and cerebral disease causes of human death. Precisely predicting stroke effect from a set of predictive attributes may classify high-risk patients and guide cure approaches, leading to reduce relative incidence. In respect to, we have collected the information regarding brain stroke patient's data from five renowned hospitals in Bangladesh with connectivity in patients with acute thalamic ischemic stroke (melanoma), Atypical Nevus (cancer risk) and Common Nevus (No cancer risk). In this work, we propose an ensemble based Modified Bootstrap Aggregating (Bagging) technique for pattern classification. Existing bagging algorithm, can usually progress the performance of a single classifier. However, they typically need larger space as well as quite time-consuming predictions. However, our proposed accuracy based pruning bagging method can improve the classification performance and reduce ensemble size. In general, our proposed modified bagging technique is more appropriate than traditional bagging technique for the prediction of brain stroke disease patients with greater accuracy of 96%.