Classification Model For Identifying Eye Defects Using Machine Learning

Electrical Electronics Engineering | Industrial Physics | Ophthalmology and Optometry

Developing a robust classification model utilizing machine learning techniques is imperative in accurately detecting and categorizing various eye defects, such as myopia, hyperopia, astigmatism, and more. Leveraging advanced algorithms and extensive datasets, this research aims to enhance the accuracy and efficiency of identifying and classifying eye abnormalities. By integrating features such as corneal curvature, axial length, and refractive errors, the model can effectively distinguish between different eye conditions, facilitating early diagnosis and personalized treatment strategies. Through continuous refinement and optimization, this approach holds promise in revolutionizing eye healthcare by enabling timely interventions and improving patient outcomes.

Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis of eye defect. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple features. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate prediction of disease diagnosis based on symptoms using machine learning algorithms. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, multiple machine learning algorithms including Decision Tree, Random Forest, Naive Bayes and Neural Network algorithms were used to analyze patient data based on multiple features, including age, illness history and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily, given a sufficient amount of data. Owing to a structured data arrangement, the random forest and decision tree algorithms’ prediction rate is more than 90% as compared to more complex methods such as neural networks and the naïve Bayes algorithm.

TABLE OF CONTENTS

COVER PAGE

TITLE PAGE

APPROVAL PAGE

DEDICATION

ACKNOWELDGEMENT

ABSTRACT

CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND OF THE PROJECT

AIM OF THE PROJECT
OBJECTIVES OF THE PROJECT
PROJECT ORGANISATION

CHAPTER TWO

2.0 LITERATURE REVIEW

OVERVIEW OF THE STUDY
RETINAL DISEASES
DIAGNOSIS METHODS
RELATED WORK

CHAPTER THREE

METHODOLOGY

INTRODUCTION
DATA MODELING
DATA PRE-PROCESSING
METHODS
MODEL DESCRIPTION

CHAPTER FOUR

RESULT ANALYSIS

RESULTS
DISCUSSION

CHAPTER FIVE

CONCLUSION
REFERENCES

CHAPTER ONE

1.0 INTRODUCTION

1.1 BACKGROUND OF THE PROJECT

Artificial intelligence (AI) plays an important role in assisting medical experts with early disease diagnosis. There are a large number of AI-based disease detection and classification systems combining medical test results and domain knowledge [1–6]. However, correlating the actual symptoms and clinical observations with the corresponding diseases is missing in most of these systems. This is perhaps owing to the variety of observation recording methods by medical experts. For example, some use symbols for diagnosis, whereas others give a textual description; hence, there is no standard method. Therefore, this data should be manually converted into a standard format so that machines can use it for analysis. This limits the size of data used in any analytical study, which is the main cause of current gaps in human-knowledge-based diagnosis and machine-intelligence-based predictions.

Commonly, ophthalmic diseases are not life threatening; however, progress over time can have significant impact on the patient’s life. Physical examinations are performed using ophthalmological instruments and a comprehensive interpretation is used for diagnosis. Therefore, any machine-based solution should concurrently consider observations, symptoms and standardized test results for predictions. Furthermore, the use of a standard description for clinical data and medical test results can be the key to success. The first step toward this is the use of health records in electronic form. Maintaining patient information as digital data has several potential benefits, including rapid retrieval along with timely data transmission among multiple medical experts [7]. Moreover, the use of standard taxonomies for patient data recording can further improve its quality, accuracy and consistency.

This study focuses on developing a general framework for the standardized recording of patient symptoms and clinical observations, thus assisting medical experts in keeping up with the exponential development of medical knowledge arising from clinical trials and logical advancements in the field. Similarly, medical cases solved in the past may greatly contribute to the training of machine-learning agents for accurate diagnosis [8]. This is also important because machine-learning algorithms can analyze the large number of features required for diagnosis more effectively than humans. Accordingly, intelligent agents, using a carefully designed multi-agent-based classification model, can outperform humans by efficiently analyzing all the features along with previous information [9].

1.1 AIM OF THE PROJECT

The aim of this study is to develop a general framework for identifying eye defects based on symptoms using machine learning algorithms.

1.2 OBJECTIVES OF THE PROJECT

At the end of this work, students involved shall be able to achieve the following objectives:

All model using machine learning algorithms for detecting eye defect were studied.
Different eye problems were studied.

Data are collected and its analysis is used for disease prediction by comparison with expert diagnosis for correct classification

1.3 PROJECT ORGANISATION

The rest of the paper is organized as follows. In Section 2, existing machine-based solutions for medical diagnosis are briefly described. In Section 3, the proposed methods for data modeling and pre-processing are explained, as well as the framework designed for analyzing and predicting eye diseases. In Section 4, the results are described and the paper is concluded in Section 5.