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README.md

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----
-language:
-- en
-tags:
-- heart
-- tabular_classification
-- binary_classification
-pretty_name: Heart
-size_categories:
-- 100<n<1K
-task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
-- tabular-classification
-configs:
-- cleveland
-- va
-- switzerland
-- hungary
----
-# Heart
-The [Heart dataset](https://archive.ics.uci.edu/ml/datasets/Heart) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
-Does the patient have heart disease?
-
-# Configurations and tasks
-| **Configuration** | **Task**                  |
-|-------------------|---------------------------|
-| cleveland         | Binary classification     |
-| va                | Binary classification     |
-| switzerland       | Binary classification     |
-| hungary           | Binary classification     |
-
-
-# Usage
-```python
-from datasets import load_dataset
-
-dataset = load_dataset("mstz/heart", "cleveland")["train"]
-```

heart-disease.names

@@ -1,245 +0,0 @@
-Publication Request: 
-   >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
-   This file describes the contents of the heart-disease directory.
-
-   This directory contains 4 databases concerning heart disease diagnosis.
-   All attributes are numeric-valued.  The data was collected from the
-   four following locations:
-
-     1. Cleveland Clinic Foundation (cleveland.data)
-     2. Hungarian Institute of Cardiology, Budapest (hungarian.data)
-     3. V.A. Medical Center, Long Beach, CA (long-beach-va.data)
-     4. University Hospital, Zurich, Switzerland (switzerland.data)
-
-   Each database has the same instance format.  While the databases have 76
-   raw attributes, only 14 of them are actually used.  Thus I've taken the
-   liberty of making 2 copies of each database: one with all the attributes
-   and 1 with the 14 attributes actually used in past experiments.
-
-   The authors of the databases have requested:
-
-      ...that any publications resulting from the use of the data include the 
-      names of the principal investigator responsible for the data collection
-      at each institution.  They would be:
-
-       1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
-       2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
-       3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
-       4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation:
-	  Robert Detrano, M.D., Ph.D.
-
-   Thanks in advance for abiding by this request.
-
-   David Aha
-   July 22, 1988
-   >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
-
-1. Title: Heart Disease Databases
-
-2. Source Information:
-   (a) Creators: 
-       -- 1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
-       -- 2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
-       -- 3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
-       -- 4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation:
-             Robert Detrano, M.D., Ph.D.
-   (b) Donor: David W. Aha ([email protected]) (714) 856-8779   
-   (c) Date: July, 1988
-
-3. Past Usage:
-    1. Detrano,~R., Janosi,~A., Steinbrunn,~W., Pfisterer,~M., Schmid,~J.,
-       Sandhu,~S., Guppy,~K., Lee,~S., \& Froelicher,~V. (1989).  {\it 
-       International application of a new probability algorithm for the 
-       diagnosis of coronary artery disease.}  {\it American Journal of 
-       Cardiology}, {\it 64},304--310.
-       -- International Probability Analysis 
-       -- Address: Robert Detrano, M.D.
-                   Cardiology 111-C
-                   V.A. Medical Center
-                   5901 E. 7th Street
-                   Long Beach, CA 90028
-       -- Results in percent accuracy: (for 0.5 probability threshold)
-             Data Name:  CDF    CADENZA
-          -- Hungarian   77     74
-             Long beach  79     77
-             Swiss       81     81
-          -- Approximately a 77% correct classification accuracy with a
-             logistic-regression-derived discriminant function
-    2. David W. Aha & Dennis Kibler
-       -- 
-          
-          
-          -- Instance-based prediction of heart-disease presence with the 
-             Cleveland database
-             -- NTgrowth: 77.0% accuracy
-             --       C4: 74.8% accuracy
-    3. John Gennari
-       -- Gennari, J.~H., Langley, P, \& Fisher, D. (1989). Models of
-          incremental concept formation. {\it Artificial Intelligence, 40},
-          11--61.
-       -- Results: 
-          -- The CLASSIT conceptual clustering system achieved a 78.9% accuracy
-             on the Cleveland database.
-
-4. Relevant Information:
-     This database contains 76 attributes, but all published experiments
-     refer to using a subset of 14 of them.  In particular, the Cleveland
-     database is the only one that has been used by ML researchers to 
-     this date.  The "goal" field refers to the presence of heart disease
-     in the patient.  It is integer valued from 0 (no presence) to 4.
-     Experiments with the Cleveland database have concentrated on simply
-     attempting to distinguish presence (values 1,2,3,4) from absence (value
-     0).  
-   
-     The names and social security numbers of the patients were recently 
-     removed from the database, replaced with dummy values.
-
-     One file has been "processed", that one containing the Cleveland 
-     database.  All four unprocessed files also exist in this directory.
-    
-5. Number of Instances: 
-        Database:    # of instances:
-          Cleveland: 303
-          Hungarian: 294
-        Switzerland: 123
-      Long Beach VA: 200
-
-6. Number of Attributes: 76 (including the predicted attribute)
-
-7. Attribute Information:
-   -- Only 14 used
-      -- 1. #3  (age)       
-      -- 2. #4  (sex)       
-      -- 3. #9  (cp)        
-      -- 4. #10 (trestbps)  
-      -- 5. #12 (chol)      
-      -- 6. #16 (fbs)       
-      -- 7. #19 (restecg)   
-      -- 8. #32 (thalach)   
-      -- 9. #38 (exang)     
-      -- 10. #40 (oldpeak)   
-      -- 11. #41 (slope)     
-      -- 12. #44 (ca)        
-      -- 13. #51 (thal)      
-      -- 14. #58 (num)       (the predicted attribute)
-
-   -- Complete attribute documentation:
-      1 id: patient identification number
-      2 ccf: social security number (I replaced this with a dummy value of 0)
-      3 age: age in years
-      4 sex: sex (1 = male; 0 = female)
-      5 painloc: chest pain location (1 = substernal; 0 = otherwise)
-      6 painexer (1 = provoked by exertion; 0 = otherwise)
-      7 relrest (1 = relieved after rest; 0 = otherwise)
-      8 pncaden (sum of 5, 6, and 7)
-      9 cp: chest pain type
-        -- Value 1: typical angina
-        -- Value 2: atypical angina
-        -- Value 3: non-anginal pain
-        -- Value 4: asymptomatic
-     10 trestbps: resting blood pressure (in mm Hg on admission to the 
-        hospital)
-     11 htn
-     12 chol: serum cholestoral in mg/dl
-     13 smoke: I believe this is 1 = yes; 0 = no (is or is not a smoker)
-     14 cigs (cigarettes per day)
-     15 years (number of years as a smoker)
-     16 fbs: (fasting blood sugar > 120 mg/dl)  (1 = true; 0 = false)
-     17 dm (1 = history of diabetes; 0 = no such history)
-     18 famhist: family history of coronary artery disease (1 = yes; 0 = no)
-     19 restecg: resting electrocardiographic results
-        -- Value 0: normal
-        -- Value 1: having ST-T wave abnormality (T wave inversions and/or ST 
-                    elevation or depression of > 0.05 mV)
-        -- Value 2: showing probable or definite left ventricular hypertrophy
-                    by Estes' criteria
-     20 ekgmo (month of exercise ECG reading)
-     21 ekgday(day of exercise ECG reading)
-     22 ekgyr (year of exercise ECG reading)
-     23 dig (digitalis used furing exercise ECG: 1 = yes; 0 = no)
-     24 prop (Beta blocker used during exercise ECG: 1 = yes; 0 = no)
-     25 nitr (nitrates used during exercise ECG: 1 = yes; 0 = no)
-     26 pro (calcium channel blocker used during exercise ECG: 1 = yes; 0 = no)
-     27 diuretic (diuretic used used during exercise ECG: 1 = yes; 0 = no)
-     28 proto: exercise protocol
-          1 = Bruce     
-          2 = Kottus
-          3 = McHenry
-          4 = fast Balke
-          5 = Balke
-          6 = Noughton 
-          7 = bike 150 kpa min/min  (Not sure if "kpa min/min" is what was 
-              written!)
-          8 = bike 125 kpa min/min  
-          9 = bike 100 kpa min/min
-         10 = bike 75 kpa min/min
-         11 = bike 50 kpa min/min
-         12 = arm ergometer
-     29 thaldur: duration of exercise test in minutes
-     30 thaltime: time when ST measure depression was noted
-     31 met: mets achieved
-     32 thalach: maximum heart rate achieved
-     33 thalrest: resting heart rate
-     34 tpeakbps: peak exercise blood pressure (first of 2 parts)
-     35 tpeakbpd: peak exercise blood pressure (second of 2 parts)
-     36 dummy
-     37 trestbpd: resting blood pressure
-     38 exang: exercise induced angina (1 = yes; 0 = no)
-     39 xhypo: (1 = yes; 0 = no)
-     40 oldpeak = ST depression induced by exercise relative to rest
-     41 slope: the slope of the peak exercise ST segment
-        -- Value 1: upsloping
-        -- Value 2: flat
-        -- Value 3: downsloping
-     42 rldv5: height at rest
-     43 rldv5e: height at peak exercise
-     44 ca: number of major vessels (0-3) colored by flourosopy
-     45 restckm: irrelevant
-     46 exerckm: irrelevant
-     47 restef: rest raidonuclid (sp?) ejection fraction
-     48 restwm: rest wall (sp?) motion abnormality
-        0 = none
-        1 = mild or moderate
-        2 = moderate or severe
-        3 = akinesis or dyskmem (sp?)
-     49 exeref: exercise radinalid (sp?) ejection fraction
-     50 exerwm: exercise wall (sp?) motion 
-     51 thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
-     52 thalsev: not used
-     53 thalpul: not used
-     54 earlobe: not used
-     55 cmo: month of cardiac cath (sp?)  (perhaps "call")
-     56 cday: day of cardiac cath (sp?)
-     57 cyr: year of cardiac cath (sp?)
-     58 num: diagnosis of heart disease (angiographic disease status)
-        -- Value 0: < 50% diameter narrowing
-        -- Value 1: > 50% diameter narrowing
-        (in any major vessel: attributes 59 through 68 are vessels)
-     59 lmt
-     60 ladprox
-     61 laddist
-     62 diag
-     63 cxmain
-     64 ramus
-     65 om1
-     66 om2
-     67 rcaprox
-     68 rcadist
-     69 lvx1: not used
-     70 lvx2: not used
-     71 lvx3: not used
-     72 lvx4: not used
-     73 lvf: not used
-     74 cathef: not used
-     75 junk: not used
-     76 name: last name of patient 
-	(I replaced this with the dummy string "name")
-
-9. Missing Attribute Values: Several.  Distinguished with value -9.0.
-
-10. Class Distribution:
-        Database:      0   1   2   3   4 Total
-          Cleveland: 164  55  36  35  13   303
-          Hungarian: 188  37  26  28  15   294
-        Switzerland:   8  48  32  30   5   123
-      Long Beach VA:  51  56  41  42  10   200

heart.py

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-"""Heart"""
-
-from typing import List
-from functools import partial
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-_BASE_FEATURE_NAMES = [
-    "age",
-    "is_male",
-    "type_of_chest_pain",
-    "resting_blood_pressure",
-    "serum_cholesterol",
-    "fasting_blood_sugar",
-    "rest_electrocardiographic_type",
-    "maximum_heart_rate",
-    "has_exercise_induced_angina",
-    "depression_induced_by_exercise",
-    "slope_of_peak_exercise",
-    "number_of_major_vessels_colored_by_flourosopy",
-    "thal",
-    "has_hearth_disease"
-]
-
-DESCRIPTION = "Heart dataset from the UCI ML repository."
-_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Heart"
-_URLS = ("https://huggingface.co/datasets/mstz/heart/raw/heart.csv")
-_CITATION = """
-@misc{misc_heart_disease_45,
-  author       = {Janosi,Andras, Steinbrunn,William, Pfisterer,Matthias, Detrano,Robert & M.D.,M.D.},
-  title        = {{Heart Disease}},
-  year         = {1988},
-  howpublished = {UCI Machine Learning Repository},
-  note         = {{DOI}: \\url{10.24432/C52P4X}}
-}"""
-
-# Dataset info
-urls_per_split = {
-    "hungary": {"train": "https://huggingface.co/datasets/mstz/heart/raw/main/processed.hungarian.data"},
-}
-features_types_per_config = {
-    "hungary": {
-        "age": datasets.Value("int8"),
-        "is_male": datasets.Value("bool"),
-        "type_of_chest_pain": datasets.Value("string"),
-        "resting_blood_pressure": datasets.Value("float32"),
-        "serum_cholesterol": datasets.Value("float32"),
-        "fasting_blood_sugar": datasets.Value("float32"),
-        "rest_electrocardiographic_type": datasets.Value("string"),
-        "maximum_heart_rate": datasets.Value("float32"),
-        "has_exercise_induced_angina": datasets.Value("bool"),
-        "depression_induced_by_exercise": datasets.Value("float32"),
-        "has_hearth_disease": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
-    },
-}
-features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
-
-_ENCODING_DICS = {
-    "type_of_chest_pain": {
-        1: "typical angina",
-        2: "atypical angina",
-        3: "non-anginal pain",
-        4: "asymptomatic"
-    }
-}
-
-class HeartConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(HeartConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class Heart(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "hungary"
-    BUILDER_CONFIGS = [
-        HeartConfig(name="hungary",
-                    description="Heart for binary classification, hungary dataset.")
-    ]
-
-
-    def _info(self):
-        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
-                                    features=features_per_config[self.config.name])
-
-        return info
-    
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        downloads = dl_manager.download_and_extract(urls_per_split)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads[self.config.name]["train"]})
-        ]
-    
-    def _generate_examples(self, filepath: str):
-        data = pandas.read_csv(filepath, header=None)
-        data.columns = _BASE_FEATURE_NAMES
-        data = self.preprocess(data, self.config.name)
-
-        for row_id, row in data.iterrows():
-            data_row = dict(row)
-
-            yield row_id, data_row
-
-    def preprocess(self, data, config):
-        for feature in _ENCODING_DICS:
-            encoding_function = partial(self.encode, feature)
-            data.loc[:, feature] = data[feature].apply(encoding_function)
-
-        data[["age"]].applymap(int)
-
-        data.drop("slope_of_peak_exercise", axis="columns", inplace=True)
-        data.drop("number_of_major_vessels_colored_by_flourosopy", axis="columns", inplace=True)
-        data.drop("thal", axis="columns", inplace=True)
-        data = data[data.serum_cholesterol != "?"]
-
-        data = data.infer_objects()
-
-        data = data[data.resting_blood_pressure != "?"]
-        data = data[data.fasting_blood_sugar != "?"]
-        data = data[data.rest_electrocardiographic_type != "?"]
-        data = data[data.maximum_heart_rate != "?"]
-        data = data[data.has_exercise_induced_angina != "?"]
-
-        data = data.astype({"is_male": bool, "has_exercise_induced_angina": bool,
-                            "serum_cholesterol": float, "maximum_heart_rate": float,
-                            "resting_blood_pressure": float, "fasting_blood_sugar": float})       
-
-        return data
-
-    def encode(self, feature, value):
-        if feature in _ENCODING_DICS:
-            return _ENCODING_DICS[feature][value]
-        raise ValueError(f"Unknown feature: {feature}")

hungary/heart-train.parquet

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+oid sha256:d28ce5ba81206251470ee617910fe06154c1e9bdf8a8ab4017f633ebb6a79ec4
+size 8472

processed.cleveland.data

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processed.hungarian.data

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processed.switzerland.data

@@ -1,123 +0,0 @@
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processed.va.data

@@ -1,200 +0,0 @@
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