Ekramul Hossain

TL;DR: It is found that the Support Vector Machine (SVM) algorithm is applied most frequently (in 29 studies) followed by the Naïve Bayes algorithm (in 23 studies), however, the Random Forest algorithm showed superior accuracy comparatively.

TL;DR: A risk prediction model utilising administrative data that uses network-based features and machine learning techniques to assess the risk of CVD in T2D patients could be useful for medical practice as well as stakeholders to develop health management programs for patients at a high risk of developing chronic diseases.

TL;DR: The proposed network-based model may potentially help the healthcare provider to understand high-risk diseases and the progression patterns between the recurrence of T2DM and CVD and could be useful for stakeholders including governments and private health insurers to adopt appropriate preventive health management programs for patients at a high risk of developing multiple chronic diseases.

TL;DR: The bispectral speech intelligibility metric (BSIM) was developed by extracting the features from the spectrogram of speech signals using the third-order statistics, which are collectively known as the bispectrum and can successfully predict nonlinear distortions, as well as time domain distortions, such as phase-jitter and reverberation.

TL;DR: The results show that chronic pulmonary disease, cardiac arrhythmias, valvular disease and renal failure occurred frequently during the progression of CHF for T2D patients, indicating that these two diseases may be potential risk factors for the progression towards CHF in patients with T1D for this population cohort.