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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Reshma Verma
Ramaiah Institute of Technology
Computer Science & IT Engineering

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Early prediction of diabetes diagnosis using hybrid classification techniques Lakshmi Srinivasan; Reshma Verma; Mysore Dakshinamurthy Nandeesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1139-1148

Abstract

Diabetes can be mentioned as one of the most lethal and constant sicknesses that may cause an arise in the glucose levels. Design and development of performance efficient diagnosis tool is important and plays a vigorous role in initial prediction of disease and help medical experts to start with suitable treatment or medication. The insulin produced by pancreases in the subject’s body will be affected leading to several dysfunctionalities to various body organs such as kidney, heart eyes and nervous system with their normal functionalities. Hence, preliminary stage detection with proper care and medication could reduce the risk of these problems. In the area of medicine to discover patient’s data as well as to attain a predictive model or a set of rules, classification techniques have been continuously used. This study helped diagnose diabetes by selecting three important artificial intelligence (AI) techniques namely the optimal decision tree algorithm model, Type-2 fuzzy expert system and adaptive neuro fuzzy inference system which is modified. In the present research work, a hybrid model is proposed in order to improve the classification prediction and accuracy. The Pima Indian diabetes dataset (PIDD) from machine learning repository dataset was used to carry out validation and predication of the model accuracy.
Machine learning classifiers for detection of glaucoma Reshma Verma; Lakshmi Shrinivasan; Basvaraj Hiremath
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp806-814

Abstract

Glaucoma is a disease that affects the optic nerve. This disease, over a period of time, can lead to loss of vision. Which is known as ‘silent thief of sight’. There are several methods in which the disease can be treated, if detected at an early stage It is not possible for any technology, including artificial intelligence, to replace a doctor. However, it is possible to develop a model based on several classical image processing algorithms, combined with artificial intelligence that can detect onset of glaucoma based on certain parameters of the retinal fundus. This model would play an important role in early detection of the disease and assist the doctor. The traditional methods to detect glaucoma, as efficient as they may be, are usually expensive, a machine learning approach to diagnose from fundus images and accurately classify its severity can be considered to be efficient. Here we propose support vector machine (SVM) method to segregate, train the models using a high-end graphics processor unit (GPU) and augment the hull convex approach to boost the accuracy of the image processing mechanisms along with distinguishing the different stages of glaucoma. A web application for the screening process has also been adopted.
Co-Authors Basvaraj Hiremath Lakshmi Shrinivasan Lakshmi Srinivasan Mysore Dakshinamurthy Nandeesh