Browsing by Author "Raihan, M."

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A Bioinformatics Approach for Identification of the Core Ontologies and Signature Genes of Pulmonary Disease and Associated Disease
(Gene Reports, Elsevier, 2021) Rehana, Hasin; Ahmed, Md Raihan; Chakma, Rana; Asaduzzaman, Sayed; Raihan, M.
Background and objective Chronic Obstructive Pulmonary Disease (COPD), Diabetes mellitus (DM), Cirrhosis (CR), Ischemic Heart Disease (IHD), Ischemic Stroke (IS), Tuberculosis (TB), Obesity (OB) diseases are related to each other. Any patient affected by any of these diseases increases the possibility of being affected by other diseases. Background studies imply that there are large numbers of similar genetic and biological features among COPD, DM, CR, IHD, IS, TB, OB. For this reason, the common gene network models among these three diseases have been explored. Methods Preprocessing and filtering has been applied to find the common genes among disease. Then the common genes or significant genes have been explored. Thirteen common genes among COPD, DM, CR, IHD, IS, TB, OB have been recognized. PPI, PDI, PCI, String Analysis and Enrichment, GRN have been carried out to imply the significant proteins, seeds, chemicals etc. Results A drug signature suggestion for the hub proteins in the PDI and PCI network. From PPIN (Generic and Tissue-Specific), GRN, GO Enrichment, String analysis with algorithm 13 most responsible hub genes are found. K-means clustering was applied to find common clusters of those 13 genes. Conclusion This analysis discovers the most substantial hub proteins based on biochemical, biological, and genetic relationships between common genes.
Prediction on Ischemic Heart Disease using Machine Learning Approaches
(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh, 2019-10) Raihan, M.; Islam, Dr. Muhammad Muinul
Ischemic heart disease (IHD) is a terrible experience that occurs when the flow of blood severely reduced or cut off due to plaque deposited on the inner wall of arteries that brings oxygen to the heart muscle, leads to the ischemic heart attack (IHA). Atherosclerosis i.e. plaque deposition on the inner wall of arteries is a silent process, has no critical symptoms to get a warning before IHD. For this reason, early detection is very important for the proper management of patients prone to IHD. In this thesis work, it was tried to predict IHD on the basis of patient history, symptoms and pathological findings of patients with heart disease using computational intelligence. Total 506 patient’s data with a maximum of 151 features including historic, symptomatic and pathologic findings were collected from AFC Fortis Escort Heart Institute, Khulna, Bangladesh. First, it was tried to identify the significant risk factors of IHD i.e. the features which are significantly correlated with IHD by applying different feature selection techniques. Then IHD was predicted using significant risk factors by applying different classifier algorithms. The significant risk factors of IHD were determined by using Chi-Square correlation, Ranking the features based on information gain and Best First Search techniques. Among 151 collected features only 28 features showed high correlations with IHD based on 0.05 significance level and information gain 1% or above. 10-fold cross-validation technique was applied with different classification algorithms e.g. Artificial Neural Network (ANN), Bagging, Logistic Regression, and Random Forest to predict IHD using the most significant 28 risk factors. IHD prediction accuracy was observed ranges from 95.85% to 97.63% with different classifier algorithm. Random Forest showed the best prediction performance with an accuracy of 97.63%. The same processing technique and classification algorithms were applied to the Cleveland hospital dataset to validate our prediction approach. The observed IHD prediction accuracy was 80.46-83.77% without applying the proposed processing techniques, but the accuracy degraded to 79.80-81.46% applying the proposed processing techniques. The Cleveland hospital data contains 303 patients’ data with only 13 features whereas the collected dataset contains 506 patient’s data with 28 nicely correlated IHD risk factors. This is why the proposed method is not suitably applicable to Cleveland dataset.
Risk Factors Categorizations of Ischemic Heart Disease in South-Western Bangladesh
(China Science Publishing & Media Ltd., 2024-09-06) Raihan, M.; Azam, Sami; Akter, Laboni; Hassan, Mehedi; Quadir, Ryana; Karim, Asif; Mondal, Saikat; More, Arun
Ischemic heart disease (IHD) is one of the leading causes of death worldwide. However, different geographic regions show different variations of the risk factors of this disease based on the different lifestyles of people. This study examines the current IHD condition in southern Bangladesh, a Southeast Asian middle-income country. The main approach to this research is an AI-based proposal of a reduced set of the greatest impact clinical traits that may cause IHD. This approach attempts to reduce IHD morbidity and mortality by early detection of risk factors using the reduced set of clinical data. Demographic, diagnostic, and symptomatic features were considered for analysing this clinical data. Data pre-processing utilizes several machine learning techniques to select significant features and make meaningful interpretations. A proposed voting mechanism ranked the selected 138 features by their impact factor. In this regard, diverse patterns in correlations with variables, including age, sex, career, family history, obesity, etc., were calculated and explained in terms of voting scores. Among the 138 risk factors, three labels were categorized: high-risk, medium-risk, and low-risk features; 19 features were regarded as high, 25 were medium, and 94 were considered low impactful features. This research’s technological methodology and practical goals provide an innovative and resilient framework for addressing IHD, especially in less developed cities and townships of Bangladesh, where the general population’s socio-economic conditions are often unexpected. The data collection, pre-processing, and use of this study’s complete and comprehensive IHD patient dataset is another innovative addition. We believe that other relevant research initiatives will benefit from this work.