Machine learning app for pigmented skin lesion diagnosis prediction with HAM10000 dataset (https://www.nature.com/articles/sdata2018161).

*Statistics
Testing with unseen HAM10000 data (1992 cases), the statistical result is as follow:

Overall accuracy: 76.66% (1527/1992)

akiec sensitivity: 45.21%(33/(33+40))
akiec precision: 58.93% (33/(33+23))

bcc sensitivity: 55.86%(62/(62+49))
bcc precision: 63.27% (62/(62+36))

bkl sensitivity: 49.33%(110/(110+113))
bkl precision: 58.20% (110/(110+79))

df sensitivity: 31.82%(7/(7+15))
df precision: 58.33% (7/(7+5))

mel sensitivity: 32.75%(75/(75+154))
mel precision: 50.34% (75/(75+74))

nv sensitivity: 94.27%(1234/(1234+75))
nv precision: 83.49% (1234/(1234+244))

vasc sensitivity: 24.00%(6/(6+19))
vasc precision: 60.00% (6/(6+4))

*Disclaimer
Before using this machine learning app, users must understand and agree as follows:
1. This app is developed by using HAM10000 dataset so the accuracy of the dataset will effect app's correctness.
2. This app has overall accuracy 76.66% when testing with HAM10000 dataset. To use with other clinical data, this value can be varied.
3. This app calculates the chance of disease occurrence with probability value. It is your responsibility to understand and using this risk value.
4. Even we try our best to develop the app, we will not be liable for any damages that you may suffer from using this app.

*Abbreviations
akiec: actinic keratoses and intraepithelial carcinoma/bowen's disease
bcc: basal cell carcinoma
bkl: benign keratosis-like lesions (solar lentigines/seborrheic keratoses and lichen-planus like keratoses)
df: dermatofibroma
mel: melanoma
nv: melanocytic nevi
vasc: vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage)