This Project provides an analysis of hospital visit records of 51,000 patients, highlighting demographic trends, health conditions, medications, and other healthcare utilization patterns. It offers insights into patient distributions across genders, age groups, and medical conditions.
Patient_ID: Unique identifier for each patient.
Age: Age of the patient in years (18-89).
Gender: The gender of the patient (Male, Female, Other).
Blood_Pressure_Systolic/Diastolic: Blood pressure measurements in mmHg.
Heart_Rate: Heart rate in beats per minute (BPM).
Cholesterol_Level: Cholesterol levels in mg/dL.
Medical_Conditions: Common medical conditions such as Diabetes, Hypertension, Asthma, or None.
Medications: Types of medication, including Metformin, Lisinopril, Statins, or None.
Visit_Date: Date of the most recent medical visit within the current year.
Diagnosis: Type of medical diagnosis (Routine Check, Infection, Emergency, Follow-up).
Hospital_Visits_Past_Year: The number of hospital visits in the past year (0-9).
BMI: Body Mass Index (18.5 to 35).
Smoker_Status: Whether the patient is a smoker (Yes/No).
Physical_Activity_Level: The patient's physical activity level (Sedentary, Moderate, Active).
- Python
- Pandas
- Matplotlib
- Seaborn
Patient visits are balanced across genders, with slight variations in chronic conditions. Males lead in diabetes, while females show higher prevalence in asthma and hypertension, especially in older age groups.
Peak Visits: Young adults (18–29), primarily for asthma, diabetes, and hypertension.
Decline with Age: Visits decrease gradually in middle and older age groups, reflecting chronic condition management.
Diabetes (12,986) and Hypertension (12,824) are most prevalent, necessitating focused care.
Asthma (2,615), though less common, requires attention for chronic respiratory management.
Statins, Metformin, and Lisinopril are equally prescribed, with many patients managing conditions without medication.
"No Medication" group suggests emphasis on lifestyle interventions and preventive care.
Heart Rate: Mostly normal (60–100 bpm) with peaks due to stress or conditions like diabetes and hypertension.
Cholesterol: Borderline high median indicates cardiovascular risk, requiring dietary and lifestyle adjustments.
BMI: Median falls in the overweight range, highlighting the need for weight management across all ages.
Lifestyle Risks: Smoking is prevalent across chronic conditions, amplifying health risks and emphasizing the need for cessation programs.
Balanced across emergencies, routine check-ups, and follow-ups, reflecting comprehensive resource allocation.
- Processed and explored hospital visit records of 51,000 patients using Python with Pandas, providing insights into gender parity, healthcare utilization patterns, and chronic condition prevalence.
- Created compelling visualizations with Seaborn and Matplotlib to depict demographic distributions, medication prescriptions, and condition-specific healthcare insights.
- Leveraged Python to identify trends in asthma, hypertension, and diabetes across age groups and genders, highlighting gaps in preventive care and the impact of lifestyle factors.
- Analyzed prescription data using Pandas, uncovering relationships between medications like Statins, Metformin, and Lisinopril and their usage across medical conditions.
- Generated data-driven reports using Python to present findings on cardiovascular risks, BMI trends, and smoking prevalence, providing actionable insights to support healthcare decision-making.
- Tools Used: Leveraged Jupyter Notebook for interactive data analysis, visualization, and documentation, ensuring clear and comprehensive communication of findings.