[ad_1]

Introduction

Healthcare systems worldwide are facing increasing pressure to reduce hospital readmissions, as they not only contribute to rising healthcare costs but also indicate suboptimal patient care and poor healthcare outcomes. Predicting patient readmissions using hospital data has the potential to improve patient outcomes, optimize resource allocation, and enhance the quality of care provided by healthcare providers.

This thesis aims to explore the predictive capabilities of hospital data in forecasting patient readmissions. Specifically, the study will investigate the factors that contribute to hospital readmissions, develop predictive models using machine learning algorithms, and evaluate the performance of these models in predicting patient readmissions accurately.

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 Patient Readmissions
2.2 Factors Contributing to Patient Readmissions
2.3 Previous Studies on Predicting Patient Readmissions
2.4 Machine Learning Algorithms for Predictive Modeling
2.5 Data Sources for Predicting Patient Readmissions
2.6 Evaluation Metrics for Predictive Models
2.7 Strategies to Reduce Hospital Readmissions
2.8 Role of Healthcare Providers in Preventing Readmissions
2.9 Ethical Considerations in Predictive Modeling
2.10 Gaps in Existing Literature

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Model Development
3.6 Model Evaluation
3.7 Performance Metrics
3.8 Ethical Approval

Chapter 4: Discussion of Findings
4.1 Descriptive Analysis of Hospital Data
4.2 Factors Influencing Readmissions
4.3 Performance of Predictive Models
4.4 Comparison of Machine Learning Algorithms
4.5 Implications for Healthcare Providers
4.6 Recommendations for Improving Predictive Modeling
4.7 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Implications for Healthcare Policy and Practice
5.3 Limitations of the Study
5.4 Suggestions for Future Research
5.5 Concluding Remarks

Thesis Overview

The healthcare industry continues to face challenges in reducing hospital readmissions, which not only pose financial burdens on healthcare systems but also indicate suboptimal patient care and outcomes. Predicting patient readmissions using hospital data has emerged as a promising approach to address this issue by leveraging machine learning algorithms to forecast readmissions accurately.

This thesis aims to explore the predictive capabilities of hospital data in forecasting patient readmissions, with a focus on identifying the factors contributing to readmissions, developing predictive models, and evaluating their performance. The study will also investigate the role of healthcare providers in preventing readmissions and suggest strategies to improve predictive modeling in healthcare settings.

By analyzing hospital data and developing predictive models, this research seeks to enhance the understanding of patient readmissions, optimize resource allocation, and improve the quality of care provided to patients. Ultimately, the findings of this study aim to inform healthcare policy and practice, leading to better patient outcomes and reduced hospital readmissions.

[ad_2]

---

Purchase Detail

Download the complete project materials to this project with Abstract, Chapters 1 – 5, References and Appendix (Questionaire, Charts, etc), Click Here to place an order via whatsapp. Got question or enquiry; Click here to chat us up via Whatsapp.
You can also call 08111770269 or +2348059541956 to place an order or use the whatsapp button below to chat us up.
Bank details are stated below.

Bank: UBA
Account No: 1021412898
Account Name: Starnet Innovations Limited

The Blazingprojects Mobile App

Download and install the Blazingprojects Mobile App from Google Play to enjoy over 50,000 project topics and materials from 73 departments, completely offline (no internet needed) with monthly update to topics, click here to install.