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Introduction

Predicting patient medication adherence is a crucial aspect of healthcare that can significantly impact patient outcomes and healthcare costs. Medication non-adherence is a widespread issue that can lead to poor health outcomes, increased hospitalizations, and higher healthcare costs. Machine learning techniques have shown promise in predicting and improving medication adherence by analyzing prescription data and identifying patterns that can help healthcare providers intervene early.

This thesis aims to investigate the use of machine learning algorithms on prescription data to predict patient medication adherence. The study will explore how various factors such as demographic information, prescription history, and healthcare utilization can be used to build predictive models for medication adherence. By accurately predicting which patients are at risk of non-adherence, healthcare providers can intervene proactively to improve patient outcomes and reduce healthcare costs.

Table of Contents

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Medication Adherence
2.2 Factors Influencing Medication Adherence
2.3 Machine Learning in Healthcare
2.4 Previous Studies on Predicting Medication Adherence
2.5 Prescription Data Analysis
2.6 Research Gaps
2.7 Theoretical Framework
2.8 Conceptual Framework
2.9 Hypotheses
2.10 Summary

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Machine Learning Algorithms
3.6 Model Evaluation
3.7 Ethical Considerations
3.8 Data Analysis Techniques
3.9 Limitations of Methodology

Chapter 4: Discussion of Findings
4.1 Descriptive Statistics
4.2 Model Performance Analysis
4.3 Factors Influencing Medication Adherence
4.4 Comparison with Previous Studies
4.5 Implications for Healthcare Providers
4.6 Future Research Directions
4.7 Recommendations
4.8 Practical Implications

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusions
5.3 Contributions to Literature
5.4 Practical Implications
5.5 Limitations of Study
5.6 Recommendations for Future Research

Thesis Overview

Predicting patient medication adherence using prescription data and machine learning is a critical area of research that can have significant implications for healthcare. Medication non-adherence is a common problem that can lead to poor outcomes for patients and increased healthcare costs. This thesis aims to investigate how machine learning algorithms can be used on prescription data to predict patient adherence and identify factors that influence medication adherence.

Chapter 1 provides an introduction to the topic, background information, problem statement, objectives of the study, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter 2 presents a comprehensive review of the literature on medication adherence, factors influencing adherence, machine learning in healthcare, previous studies on predicting adherence, prescription data analysis, research gaps, theoretical and conceptual frameworks, hypotheses, and a summary.

Chapter 3 outlines the research methodology, including research design, data collection, preprocessing, feature selection, machine learning algorithms, model evaluation, ethical considerations, data analysis techniques, and limitations of the methodology. Chapter 4 discusses the findings of the study, including descriptive statistics, model performance analysis, factors influencing medication adherence, comparison with previous studies, implications for healthcare providers, future research directions, recommendations, and practical implications.

Chapter 5 summarizes the findings, draws conclusions, discusses contributions to the literature, practical implications, limitations of the study, and recommendations for future research. By predicting patient medication adherence using prescription data and machine learning, this thesis aims to provide valuable insights for healthcare providers to improve patient outcomes and reduce healthcare costs.

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