README for Predictive Healthcare Analytics: Length of Stay and Billing Predictions

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Predictive Healthcare Analytics: Length of Stay and Billing Predictions

This project delves into the realm of predictive analytics in healthcare, focusing on forecasting hospital length of stay and billing amounts based on patient demographics and medical conditions. By leveraging advanced data analysis and machine learning techniques, we aim to enhance resource allocation, patient care, and operational efficiency in healthcare settings.

Project Overview

Through the adept application of data analysis and machine learning skills, this analysis aims to achieve the following objectives:

• Import and preprocess data from various file formats.
• Clean and integrate data from multiple sources to facilitate a comprehensive analysis.
• Utilize string and date manipulation techniques to ensure data accuracy and extract meaningful insights.
• Build predictive models to estimate hospital length of stay and billing amounts.
• Develop an interactive dashboard to provide real-time predictions and support decision-making.

Tools and Techniques

The project relies on the proficient use of various tools and packages commonly employed by data analysts and scientists. The utilization of these tools reflects the significance of data analysis in today's data-driven world:

• Pandas: For data manipulation, cleaning, and analysis.
• NumPy: For numerical computing and array operations.
• Scikit-learn: For building and evaluating machine learning models.
• Matplotlib and Seaborn: For data visualization.
• Dash by Plotly: For creating interactive dashboards.

Methodology

Data Preprocessing

• Data Cleaning: Corrected inconsistent casing in columns and handled missing values.
• Datetime Conversion: Converted 'Date of Admission' and 'Discharge Date' to datetime format.
• Feature Engineering: Created 'Length of Stay' and categorized 'Age Group'.

Exploratory Data Analysis

• Descriptive Analysis: Summarized numerical and categorical features.
• Bivariate Analysis: Explored relationships between variables using scatter plots and correlation coefficients.

Model Building

• Feature Selection: Identified relevant features for predicting length of stay and billing amount.
• Categorical Encoding: Applied one-hot encoding to categorical variables.
• Linear Regression: Built and trained models to predict target variables.
• Model Evaluation: Assessed models using Mean Absolute Error (MAE).

Dashboard

Developed an interactive dashboard using Dash by Plotly to provide real-time predictions for length of stay and billing amounts. The dashboard allows users to input parameters and obtain actionable insights, facilitating data-driven decision-making in hospitals.

Findings

The predictive models yielded several key insights:

• Length of Stay: Influenced by age, medical condition, and admission type.
• Billing Amount: Affected by age group and medical condition, with chronic conditions and advanced age leading to higher costs.
• Model Accuracy: Achieved reasonable prediction accuracy with MAEs of 12189.28 for billing amount and 7.48 days for length of stay.

Conclusions and Recommendations

This project demonstrates the potential of predictive analytics in healthcare to enhance decision-making and operational efficiency. By leveraging linear regression models, we can predict crucial outcomes like length of stay and billing amounts, providing valuable insights for healthcare providers. It is recommended that hospitals implement predictive analytics to support strategic planning and daily operations. Future work could explore integrating more advanced machine learning algorithms and expanding the dataset for even more accurate predictions.