DETECTION OF CHRONIC DISEASES AND ANALYSIS OF HYPERPARAMETERS USING DEEP LEARNING TECHNIQUES

Abstract

Chronic illnesses constitute a huge global health burden, accounting for a considerable amount of morbidity and mortality. Early detection, precise diagnosis, and risk assessment are critical for improving patient outcomes and lowering healthcare costs. Deep learning and machine learning approaches have shown promise in improving the precision and efficiency of these kinds of tasks. This study focuses on the development and validation of advanced algorithms for the detection and diagnosis of chronic illnesses using machine learning and deep learning technologies. To train and fine-tune prediction models, we used public repositories’ tabular data, medical imaging, and real clinical data. The first part of this study is using supervised learning algorithms to create diseasespecific classifiers. These models seek to detect the presence or absence of chronic illnesses. The second part focuses on deep learning approaches to analyze medical images as well as tabular data, such as convolutional neural networks (CNNs), LSTM, and TabNet. On the other hand, comorbidity detection allows for better risk assessment. A validation dataset is used to assess the proposed models properly. To evaluate the model’s efficacy, performance parameters such as sensitivity, specificity, accuracy, precision, F1-Score, and area under the receiver operating characteristic curve (AUC-ROC) score are utilized. Furthermore, this study focuses on the construction of interpretable machine learning models to help doctors, clinicians, and health workers understand the underlying factors driving the predictions. Explainable AI technologies, such as feature visualization and attention mechanisms, are used to convey insights into the models’ decision-making process, increasing confidence and acceptability among healthcare practitioners. This study advances deep learning techniques in healthcare, with the ultimate goal of facilitating earlier detection and diagnosis of chronic diseases and improving patient risk assessment, resulting in more proactive and personalized medical interventions. iii The major objectives of the present work are as follows: • Systematic study and analysis of existing computational methods for the detection and diagnosis of chronic diseases. • Analysis and exploration of feature selection and hyperparameters for possible improvements and development of data balancing techniques to predict chronic disease. • Development and analysis of a multioutput classifier for the detection of comorbid diseases on a real dataset. • Development and analysis of explainable classifiers for the detection of chronic diseases. The thesis is organized into seven chapters. Chapter 1: Introduction The introduction part covers the basic idea about chronic disease and general concepts of machine learning and deep learning models, motivation, research challenges, objectives, and contributions of the thesis. Chapter 2: Analysis of Computational Methods for Chronic Disease Detection Using Hybrid Hyperparameter Optimization Algorithm. This chapter highlights the research on evaluating and assessing various computational approaches for detecting and diagnosing chronic diseases. Chronic diseases are persistent and long-lasting medical conditions that require early detection and accurate diagnosis for effective management and improved patient outcomes. The wide range of machine learning and deep learning techniques that are commonly used in healthcare research for disease prediction and diagnosis. These methods may include traditional algorithms (e.g., logistic regression, decision trees) and advanced deep learning models (e.g., convolutional neural networks, recurrent neural networks). Chapter 3: Prediction of Non-Communicable Chronic Disease using Stacking Classifier by Exploring Feature Selection and Hyperparameters Optimization. Feature selection techniques will be used to identify the most relevant predictors that contribute to disease prediction. Hyperparameter optimization focuses on fine-tuning the model’s settings to achieve optimal performance. Data balancing strategies aim to handle class imbalances in the dataset, which are common in medical datasets with fewer positive samples. The proposed techniques have the potential to advance predictive modeling in chronic disease research and ultimately iv improve patient outcomes in the healthcare domain. Chapter 4: Deep Learning Approaches for Prediction of Chronic Disease. Deep learning approaches for the prediction of chronic diseases involve the application of advanced artificial intelligence techniques to analyze healthcare data. These methods use neural networks with multiple layers to extract patterns and insights from various data sources, including electronic health records, medical images, and patient histories. The main aim of using deep learning techniques is to improve the accuracy and efficiency of chronic disease prediction, early diagnosis, and personalized treatment recommendations, ultimately leading to better patient outcomes and more effective healthcare management. Chapter 5: Comorbidity Detection: A Real-World Study Using Multioutput Classification. This chapter emphasizes with the primary objective of the study, which is to identify and analyze the presence of multiple medical conditions occurring simultaneously in patients on the basis of the real dataset. Comorbidities can significantly impact patient outcomes and treatment strategies, making their detection crucial for effective healthcare management. Multioutput classification techniques enable the simultaneous prediction of multiple target variables, allowing for a more comprehensive analysis of patients’ medical conditions. Chapter 6: Analyzing the Detection of Chronic Diseases using Explainable Approaches This chapter focuses on Explainability which refers to the ability to describe the behavior of an ML/ DL model in human terms. With complex models, it is challenging to appreciate how and why the underlying mechanics affect prediction fully. Model-agnostic techniques may be leveraged to find meaning between input data attributions and model outputs, enabling to characterize the nature and behavior of the AI/ML. Chapter 7: Conclusion and FutureWork This chapter concludes the thesis and presents the future scope of the work and future research directions.