Browsing by Author "TANZINA RAHMAN HERA"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    AN EFFECTIVE TECHNIQUE ON EARLY HEART DISEASE PREDICTION
    (2020-12-10) TANZINA RAHMAN HERA
    Heart disease is the most threatened issue in a human body. Most of the people in the world are affected by heart disease which increases the death rate of humans considerably. The detection of heart disease is the most trivial task for medical researchers which cannot be done more accurately. The early detection, manual prediction and prevention is a complex task and troublesome. So there is an urgent need for a well-designed method for the early detection of heart disease. Thus it is required to implement the automated system by considering the recent computer technologies which can help medical researchers to diagnosis heart disease firstly and accurately. Now a day’s artificial neural network has been widely used as a tool for solving many decision modelling problems. This research enlightened a number of structures in Artificial Neural Network by varying the configurations of neural network algorithms like Radial Basis Function Neural Network, Recurrent Neural Network, Decision tree classifier and Support Vector Machine Neural Network. This research also compared those Neural Network models on the early detection of heart disease and chose the best neural network model among them to detect the heart disease early. The Network Structure that showed more accuracy and efficiency is chosen among them.
  • No Thumbnail Available
    Item
    EARLY GESTATIONAL DIABETES DETECTION THROUGH NEURAL NETWORK
    (2018-07-30) TANZINA RAHMAN HERA
    Gestational Diabetes Mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. Fifty percent of GDM patients develop type 2 Diabetes in next twenty years and as well as the newborn can also be affected by diabetes in their lifetime. So the long term complications for both the mother and the child cannot be ignored. In view of maternal morbidity and mortality as well as fetal complications, early diagnosis is an utmost necessity in the present scenario. In developing country like Bangladesh, early detection and prevention is more cost effective and troublesome. So there is an urgent need for a well-designed method for the detection of gestational diabetes mellitus. The purpose of this study is to predict the GDM in the first trimester. This research presents and compares some Artificial Neural Network (ANN) models on the early detection of Gestational diabetes mellitus and chooses the best neural network model among them to detect GDM early.