Data Report — Fertility

Documentation: Season in which the analysis was performed. 1) winter, 2) spring, 3) Summer, 4) fall. (-1, -0.33, 0.33, 1)

Age at the time of analysis. 18-36 (0, 1)

Childish diseases (ie , chicken pox, measles, mumps, polio) 1) yes, 2) no. (0, 1)

Accident or serious trauma 1) yes, 2) no. (0, 1)

Surgical intervention 1) yes, 2) no. (0, 1)

High fevers in the last year 1) less than three months ago, 2) more than three months ago, 3) no. (-1, 0, 1)

Frequency of alcohol consumption 1) several times a day, 2) every day, 3) several times a week, 4) once a week, 5) hardly ever or never (0, 1)

Smoking habit 1) never, 2) occasional 3) daily. (-1, 0, 1)

Number of hours spent sitting per day ene-16 (0, 1)

Output: Diagnosis normal (N), altered (O)

Citation: {'@type': 'schema:ScholarlyArticle', 'title': 'Predicting seminal quality with artificial intelligence methods', 'schema:author': ['David Gil', 'J. L. Girela', 'Joaquin De Juan', 'M. Jose Gomez-Torres', 'Magnus Johnsson'], 'schema:isPartOf': 'Expert systems with applications', 'schema:datePublished': 2012, 'url': 'https://www.semanticscholar.org/paper/Predicting-seminal-quality-with-artificial-methods-Gil-Girela/92759c5ee08b9e6e7b17d1ccd48a7f8c02aba893'}

Source: UCI dataset 244

SemMap JSON-LD: dataset.semmap.json · RDFa HTML

Overview

Variables and summary

Fidelity summary

umap	model	backend	disc jsd mean	disc jsd median	cont ks mean	cont w1 mean	downstream sign match
	metasyn	metasyn	0.058	0.0683	0.2033	0.1181	0.25
	clg_mi2	pybnesian	0.0647	0.0362	0.1833	0.1201
	semi_mi5	pybnesian	0.0647	0.0362	0.1833	0.1201
	ctgan_fast	synthcity	0.3398	0.3911	0.6367	0.2003
	tvae_quick	synthcity	0.1168	0.1052	0.2933	0.1255

Privacy summary

Models

UMAP	Details	Structure
	Real data
	Model: metasyn (metasyn) Seed: 42, rows: 100 Missingness: wrapped (random state 42) via pipeline Synthetic CSV Per-variable metrics Metrics JSON Privacy metrics Downstream metrics Per-variable fidelity variable type KS W1 JSD season continuous 0.31 0.2876 age continuous 0.15 0.0261 child_diseases discrete 0.0245 accident discrete 0.0694 surgical_intervention discrete 0.0683 high_fevers discrete 0.069 alcohol discrete 0.0812 smoking discrete 0.0523 hrs_sitting continuous 0.15 0.0405 diagnosis discrete 0.0416 Downstream metrics metric value sign_match_rate 0.25 formula diagnosis ~ Q('season') + Q('age') + Q('child_diseases') + Q('accident') + Q('surgical_intervention') + Q('hrs_sitting') + Q('season'):Q('age') + Q('age'):Q('child_diseases') + Q('child_diseases'):Q('accident') + Q('accident'):Q('surgical_intervention') + Q('surgical_intervention'):Q('hrs_sitting') skipped_reason Privacy metrics metric value n_real 100 n_synth 100 exact_overlap_rate 0 near_duplicate_rate_eps 0.21 nn_distance_mean 0.4309 k_min 1 k_pct_lt5 1 k_map 3 rare_qi_reproduction_rate 0 delta_presence 2.6667	variable distribution season core.uniform age core.truncated_normal child_diseases core.multinoulli accident core.multinoulli surgical_intervention core.multinoulli high_fevers core.multinoulli alcohol core.multinoulli smoking core.multinoulli hrs_sitting core.normal diagnosis core.multinoulli
	Model: clg_mi2 (pybnesian) Seed: 42, rows: 100 Params: {"max_indegree": 2, "operators": ["arcs"], "score": "bic", "type": "clg"} Missingness: wrapped (random state 42) via pipeline Synthetic CSV Per-variable metrics Metrics JSON Privacy metrics Per-variable fidelity variable type KS W1 JSD season continuous 0.27 0.3014 age continuous 0.15 0.026 child_diseases discrete 0.0362 accident discrete 0 surgical_intervention discrete 0.0597 high_fevers discrete 0.0286 alcohol discrete 0.1875 smoking discrete 0.1279 hrs_sitting continuous 0.13 0.0329 diagnosis discrete 0.0133 Privacy metrics metric value n_real 100 n_synth 100 exact_overlap_rate 0 near_duplicate_rate_eps 0.22 nn_distance_mean 0.4959 k_min 1 k_pct_lt5 1 k_map 2 rare_qi_reproduction_rate 0 delta_presence 2.5
	Model: semi_mi5 (pybnesian) Seed: 42, rows: 100 Params: {"max_indegree": 5, "operators": ["arcs"], "score": "bic", "type": "semiparametric"} Missingness: wrapped (random state 42) via pipeline Synthetic CSV Per-variable metrics Metrics JSON Privacy metrics Per-variable fidelity variable type KS W1 JSD season continuous 0.27 0.3014 age continuous 0.15 0.026 child_diseases discrete 0.0362 accident discrete 0 surgical_intervention discrete 0.0597 high_fevers discrete 0.0286 alcohol discrete 0.1875 smoking discrete 0.1279 hrs_sitting continuous 0.13 0.0329 diagnosis discrete 0.0133 Privacy metrics metric value n_real 100 n_synth 100 exact_overlap_rate 0 near_duplicate_rate_eps 0.22 nn_distance_mean 0.4959 k_min 1 k_pct_lt5 1 k_map 2 rare_qi_reproduction_rate 0 delta_presence 2.5
	Model: ctgan_fast (synthcity) Seed: 42, rows: 100 Params: {"batch_size": 256, "n_iter": 5} Missingness: wrapped (random state 42) via pipeline Synthetic CSV Per-variable metrics Metrics JSON Privacy metrics Per-variable fidelity variable type KS W1 JSD season continuous 0.12 0.1127 age continuous 0.93 0.169 child_diseases discrete 0.1621 accident discrete 0.4065 surgical_intervention discrete 0.3911 high_fevers discrete 0.4932 alcohol discrete 0.266 smoking discrete 0.4694 hrs_sitting continuous 0.86 0.3192 diagnosis discrete 0.1901 Privacy metrics metric value n_real 100 n_synth 100 exact_overlap_rate 0 near_duplicate_rate_eps 0.12 nn_distance_mean 0.3655 k_min 1 k_pct_lt5 1 k_map 1 rare_qi_reproduction_rate 0 delta_presence 36
	Model: tvae_quick (synthcity) Seed: 42, rows: 100 Params: {"batch_size": 256} Missingness: wrapped (random state 42) via pipeline Synthetic CSV Per-variable metrics Metrics JSON Privacy metrics Per-variable fidelity variable type KS W1 JSD season continuous 0.21 0.2272 age continuous 0.29 0.0491 child_diseases discrete 0.1867 accident discrete 0.1052 surgical_intervention discrete 0.0856 high_fevers discrete 0.1405 alcohol discrete 0.138 smoking discrete 0.072 hrs_sitting continuous 0.38 0.1002 diagnosis discrete 0.0898 Privacy metrics metric value n_real 100 n_synth 100 exact_overlap_rate 0 near_duplicate_rate_eps 0.08 nn_distance_mean 0.3762 k_min 1 k_pct_lt5 1 k_map 1 rare_qi_reproduction_rate 0 delta_presence 7