Article
HOSPITAL ADMISSION ANALYSIS AND PREDICTION
The Hospital Admission Analysis and Prediction system is designed to analyze hospital admission data and predict the likelihood of patient admission based on historical healthcare information. Hospitals generate large amounts of patient data related to demographics, medical conditions, symptoms, previous visits, test results, and treatment information. Analyzing this data effectively can help healthcare organizations understand admission patterns and improve resource planning. The proposed system uses historical hospital records to identify important factors associated with patient admissions. The collected data is first preprocessed by handling missing values, duplicate records, inconsistent entries, and categorical attributes. Exploratory data analysis is performed to understand admission trends based on patient characteristics, medical conditions, and other relevant factors. Data visualization techniques are used to present admission statistics and patterns through charts, graphs, and dashboards. Machine-learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine can be applied to build a prediction model. The trained model analyzes relevant patient information and predicts the possible admission outcome for new cases. The performance of the prediction models can be evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The system can help hospitals estimate potential admission demand and support better allocation of beds, staff, and other resources. It can also assist healthcare administrators in identifying admission patterns and planning hospital operations more efficiently. The system is intended as a decision-support tool and not as a replacement for clinical judgment or medical diagnosis. Overall, the Hospital Admission Analysis and Prediction system demonstrates how data analytics and machine learning can be used to analyze healthcare admission patterns, generate predictions, and support efficient hospital resource planning and decision-making.
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