SECONDARY SCHOOL CERTIFCATE (SSC)

2007

Jessore

KHULNA ZILLA SCHOOL
GPA: 5.00

HIGHER SECONDARY CERTIFCATE (HSC)

2009

Jessore

GOVT. M. M. CITY COLLEGE
GPA: 5.00
Position: 282 in Jessore Board

BACHELOR OF SCIENCE (B.SC. ENG.) IN COMPUTER SCIENCE AND ENGINEERING

2016

KHULNA UNIVERSITY (KU)

Thesis Title: Heart Diseases Prediction Using Clinical Data And Data Mining Approaches

Abstract:
Heart disease is now very frequent in Bangladesh. The healthcare industry collects huge amounts of data, however that is not mined. Medical diagnosis is very important but very expensive. In our country most of our people cannot afford this expensive diagnosis cost. Thus we want to develop a smart phone based system that can initially predict heart disease risk. The clinical data from 787 patients was correlated and analyzed with the risk factors like Hypertension, Diabetes, Dyslipidemia, Smoking, Family History, Exercise, Stress and existing clinical symptom which may suggest underlying non detected IHD. The data was mined with data mining technology in computer science and a score was generated. The risk was classified into Low, Medium and High for IHD. On comparing and categorizing the patients whose data was obtained for generating the score; we found there was significant correlation of having a cardiac event when Low and High category was compared and p value = 0.0004. Our thesis is motivated to make simple approach to detect the heart disease risk and aware the population to get themselves evaluated by a cardiologist to avoid sudden deaths and morbidities. Currently available tools has mandatory input of lipid values which makes them underutilized by population though those risk calculators bear excellent academic importance. Our thesis product may reduce this limitation and promote a risk evaluation on time.

MASTER OF SCIENCE (M.SC. ENG.) IN BIOMEDICAL ENGINEERING

2019

KHULNA UNIVERSITY OF ENGINEERING AND TECHNOLOGY (KUET)

Thesis Title: Prediction on Ischemic Heart Disease using Machine Learning Approaches

Abstract:
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.

ASSISTANT TRAINER

2015

ICT MINISTRY (MOICT)

Worked as Trainer in National 500 Apps Trainer and Innovative Apps Development Program arranged by ICT Ministry (MoICT), Bangladesh.

RESEARCH FELLOW

September 2015 - 2018

RURAL HEALTH PROGRESS TRUST (RHPT)

Worked remotely as medical researcher in an Indian NGO named Rural Health Progress Trust (RHPT)

TEACHING ASSISTANT

August 2017 - December 2017

KHULNA UNIVERSITY OF ENGINEERING AND TECHNOLOGY

Worked as Teaching Assistant under Biomedical Engineering Department, Khulna University of Engineering and Technology (KUET), Khulna, Bangladesh.

PART-TIME LECTURER

September 2017 - December 2017

SOUTH EAST ENGINEERING COLLEGE

Worked as Part-time Lecturer under Computer Science & Engineering Department, South East Engineering College, Khulna an a?liated college under Rajshahi University, Bangladesh.

ADJUNCT FACULTY

September 2017 - December 2017

NORTH WESTERN UNIVERSITY

Worked as Part-time Lecturer under Computer Science & Engineering Department, North Western University, Khulna, Bangladesh.

PART-TIME LECTURER

July 2017 - December 2017

KHULNA UNIVERSITY

Worked as Part-time Lecturer under Computer Science & Engineering Discipline, Khulna University, Khulna, Bangladesh.

LECTURER

2018 - 2021

NORTH WESTERN UNIVERSITY

Worked as full time Lecturer and Faculty Member of North Western University, Khulna, Bangladesh.

SENIOR LECTURER

2021 - Current

NORTH WESTERN UNIVERSITY

Working as full time Senior Lecturer and Faculty Member of North Western University, Khulna, Bangladesh.