Eye Care Analytics

ODIR-5K · Excel pipeline · EDA & ML

System operational API health

Clinical ophthalmology data overview

Production dashboard for exploratory analysis and model outputs from the Ocular Disease Recognition dataset. Data is loaded from your local full_data.xlsx when present.

Total records
3,500
15 features
Mean age
57.9 yrs
Median 59.0 years
Gender groups
2
Patient sex categories
Visualizations
7
Charts & model artifacts

Gender breakdown

Distribution of patients by sex

Sex Count Share
Male 1,885 53.9%
Female 1,615 46.1%

Disease prevalence

Multi-label diagnostic counts (N, D, G, C, A, H, M, O)

Normal 1,140
Diabetes 1,128
Glaucoma 215
Cataract 212
AMD 164
Hypertension 103
Myopia 174
Other 979

Analytics outputs

Six EDA comparisons plus ML confusion matrix

Disease distribution

Case counts across ODIR-5K disease categories
Disease distribution
1_disease_distribution_bar.png

Gender demographics

Patient sex distribution in the dataset
Gender demographics
2_gender_pie.png

Age by disease

Box plot of patient age grouped by primary diagnosis
Age by disease
3_age_by_disease_box.png

Disease co-occurrence

Correlation matrix between diagnostic labels
Disease co-occurrence
4_disease_correlation_heatmap.png

Age distribution

Histogram of patient ages with mean and median
Age distribution
5_age_histogram.png

Disease by gender

Grouped comparison of conditions by sex
Disease by gender
6_disease_by_gender_grouped.png

Model confusion matrix

Classification performance of the best ML model
Model confusion matrix
7_confusion_matrix.png