/UCI_Dermatology_PCA_Analysis

This ipython notebook is my self learning effort, where I have performed analysis of dermatology dataset from UCI site. Here, I explore PCA and few classifier algorithms.

Primary LanguageJupyter Notebook

UCI_Dermatology_PCA_Analysis

This ipython notebook is my self learning effort, where I have performed analysis of dermatology dataset from UCI site. Here, I explore PCA and few classifier algorithms.

The dataset is taken from the free datasets found in UCI archives. Below is the full detail of the dataset.

  1. Title: Dermatology Database

  2. Source Information: (a) Original owners: -- 1. Nilsel Ilter, M.D., Ph.D., Gazi University, School of Medicine 06510 Ankara, Turkey Phone: +90 (312) 214 1080

    -- 2. H. Altay Guvenir, PhD., 
      Bilkent University,
      Department of Computer Engineering and Information Science,
      06533 Ankara, Turkey
      Phone: +90 (312) 266 4133
      Email: guvenir@cs.bilkent.edu.tr

    (b) Donor: H. Altay Guvenir, Bilkent University, Department of Computer Engineering and Information Science, 06533 Ankara, Turkey Phone: +90 (312) 266 4133 Email: guvenir@cs.bilkent.edu.tr

    (c) Date: January, 1998

  3. Past Usage:

    1. G. Demiroz, H. A. Govenir, and N. Ilter, "Learning Differential Diagnosis of Eryhemato-Squamous Diseases using Voting Feature Intervals", Aritificial Intelligence in Medicine,

      The aim is to determine the type of Eryhemato-Squamous Disease.

  4. Relevant Information: This database contains 34 attributes, 33 of which are linear valued and one of them is nominal.

    The differential diagnosis of erythemato-squamous diseases is a real problem in dermatology. They all share the clinical features of erythema and scaling, with very little differences. The diseases in this group are psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, cronic dermatitis, and pityriasis rubra pilaris. Usually a biopsy is necessary for the diagnosis but unfortunately these diseases share many histopathological features as well. Another difficulty for the differential diagnosis is that a disease may show the features of another disease at the beginning stage and may have the characteristic features at the following stages. Patients were first evaluated clinically with 12 features. Afterwards, skin samples were taken for the evaluation of 22 histopathological features. The values of the histopathological features are determined by an analysis of the samples under a microscope.

    In the dataset constructed for this domain, the family history feature has the value 1 if any of these diseases has been observed in the family, and 0 otherwise. The age feature simply represents the age of the patient. Every other feature (clinical and histopathological) was given a degree in the range of 0 to 3. Here, 0 indicates that the feature was not present, 3 indicates the largest amount possible, and 1, 2 indicate the relative intermediate values.

    The names and id numbers of the patients were recently removed from the database.

  5. Number of Instances: 366

  6. Number of Attributes: 34

  7. Attribute Information: -- Complete attribute documentation: Clinical Attributes: (take values 0, 1, 2, 3, unless otherwise indicated) 1: erythema 2: scaling 3: definite borders 4: itching 5: koebner phenomenon 6: polygonal papules 7: follicular papules 8: oral mucosal involvement 9: knee and elbow involvement 10: scalp involvement 11: family history, (0 or 1) 34: Age (linear)

    Histopathological Attributes: (take values 0, 1, 2, 3) 12: melanin incontinence 13: eosinophils in the infiltrate 14: PNL infiltrate 15: fibrosis of the papillary dermis 16: exocytosis 17: acanthosis 18: hyperkeratosis 19: parakeratosis 20: clubbing of the rete ridges 21: elongation of the rete ridges 22: thinning of the suprapapillary epidermis 23: spongiform pustule 24: munro microabcess 25: focal hypergranulosis 26: disappearance of the granular layer 27: vacuolisation and damage of basal layer 28: spongiosis 29: saw-tooth appearance of retes 30: follicular horn plug 31: perifollicular parakeratosis 32: inflammatory monoluclear inflitrate 33: band-like infiltrate

  8. Missing Attribute Values: 8 (in Age attribute). Distinguished with '?'.

  9. Class Distribution: Database: Dermatology

    Class code:   Class:                  Number of instances:
1             psoriasis			    112
2             seboreic dermatitis             61
3             lichen planus                   72
4             pityriasis rosea                49
5             cronic dermatitis               52    
6             pityriasis rubra pilaris        20