A machine learning approach for prediction of pregnancy outcome following IVF treatment
- Authors: Hassan, Md Rafiul , Al-Insaif, Sadiq , Hossain, Muhammad , Kamruzzaman, Joarder
- Date: 2020
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 32, no. 7 (2020), p. 2283-2297
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- Description: Infertility affects one out of seven couples around the world. Therefore, the best possible management of the in vitro fertilization (IVF) treatment and patient advice is crucial for both patients and medical practitioners. The ultimate concern of the patients is the success of an IVF procedure, which depends on a number of influencing attributes. Without any automated tool, it is hard for the practitioners to assess any influencing trend of the attributes and factors that might lead to a successful IVF pregnancy. This paper proposes a hill climbing feature (attribute) selection algorithm coupled with automated classification using machine learning techniques with the aim to analyze and predict IVF pregnancy in greater accuracy. Using 25 attributes, we assessed the prediction ability of IVF pregnancy success for five different machine learning models, namely multilayer perceptron (MLP), support vector machines (SVM), C4.5, classification and regression trees (CART) and random forest (RF). The prediction ability was measured in terms of widely used performance metrics, namely accuracy rate, F-measure and AUC. Feature selection algorithm reduced the number of most influential attributes to nineteen for MLP, sixteen for RF, seventeen for SVM, twelve for C4.5 and eight for CART. Overall, the most influential attributes identified are: ‘age’, ‘indication’ of fertility factor, ‘Antral Follicle Counts (AFC)’, ‘NbreM2’, ‘method of sperm collection’, ‘Chamotte’, ‘Fertilization rate in vitro’, ‘Follicles on day 14’ and ‘Embryo transfer day.’ The machine learning models trained with the selected set of features significantly improved the prediction accuracy of IVF pregnancy success to a level considerably higher than those reported in the current literature. © 2018, The Natural Computing Applications Forum.
The epidemiology of melioidosis and its association with diabetes mellitus : a systematic review and meta-analysis
- Authors: Chowdhury, Sukanta , Barai, Lovely , Afroze, Samira , Ghosh, Probir , Afroz, Farhana , Rahman, Habibur , Ghosh, Sumon , Hossain, Muhammad , Rahman, Mohammed , Das, Pritomy , Rahim, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Pathogens Vol. 11, no. 2 (2022), p.
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- Description: Melioidosis is an under-recognized fatal disease in humans, caused by the Gram-negative bacterium Burkholderia pseudomallei. Globally, more than 35,000 human melioidosis cases have been reported since 1911. Soil acts as the natural reservoir of B. pseudomallei. Humans may become infected by this pathogen through direct contact with contaminated soil and/or water. Melioidosis commonly occurs in patients with diabetes mellitus, who increase the occurrence of melioidosis in a population. We carried out a systematic review and meta-analysis to investigate to what extent diabetes mellitus affects the patient in getting melioidosis. We selected 39 articles for meta-analysis. This extensive review also provided the latest updates on the global distribution, clinical manifestation, preexisting underlying diseases, and risk factors of melioidosis. Diabetes mellitus was identified as the predominant predisposing factor for melioidosis in humans. The overall proportion of melioidosis cases having diabetes was 45.68% (95% CI: 44.8–46.57, p < 0.001). Patients with diabetes mellitus were three times more likely to develop melioidosis than patients with no diabetes (RR 3.40, 95% CI: 2.92–3.87, p < 0.001). The other potential risk factors included old age, exposure to soil and water, preexisting underlying diseases (chronic kidney disease, lung disease, heart disease, and thalassemia), and agricultural activities. Evidence-based clinical practice guidelines for melioidosis in patients with diabetes mellitus may be developed and shared with healthcare professionals of melioidosis endemic countries to reduce morbidity. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.