Blockchain-Medical-Record-System-with-Machine-Learning/process_data.py at main · cold4Fun/Blockchain-Medical-Record-System-with-Machine-Learning · GitHub

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import pandas as pd
from people import People

df = pd.read_csv('C:/Users/zjy/Desktop/Final_dataset_covid19 - Copy.csv')

num_features = [
    'age_quantile',
    'covid19_Res',
    'hemoglobin',
    'platelets',
    'MPV',
    'RBC',
    'lymphocytes',
    'MCHC',
    'leukocytes',
    'basophils',
    'MCH',
    'eosinophils',
    'MCV',
    'monocytes',
    'RDW',
    'detection_coronaviridae',
    'detection_orthomyxoviridae',
    'detection_paramyxoviridae',
    'detection_picornaviridae',
    'detection_pneumoviridae'
]
number_fea = [
    'age_quantile',
    'detection_coronaviridae',
    'detection_orthomyxoviridae',
    'detection_paramyxoviridae',
    'detection_picornaviridae',
    'detection_pneumoviridae'
]
float_features = [
    'hemoglobin',
    'platelets',
    'MPV',
    'RBC',
    'lymphocytes',
    'MCHC',
    'leukocytes',
    'basophils',
    'MCH',
    'eosinophils',
    'MCV',
    'monocytes',
    'RDW',
]


def make_people(sl):
    return People(str(sl[0]), sl[1], str(sl[2]), str(sl[3]), str(sl[4]), str(sl[5]), str(sl[6]), str(sl[7]), str(sl[8]),
                  str(sl[9]), str(sl[10]), str(sl[11]), str(sl[12]), str(sl[13]), str(sl[14]), str(sl[15]), sl[16],
                  sl[17], sl[18], sl[19])


def processing_data():
    ll = df.values.tolist()
    result = []
    for i in range(df.shape[0]):
        pp = make_people(ll[i])
        result.append(pp)
    return result


def pre_precess_for_model(ll):
    df = pd.DataFrame(ll, columns=num_features)
    for i in number_fea:
        df[[i]] = df[[i]].astype('float')
        df[[i]] = df[[i]].astype('int')
    for i in float_features:
        df[[i]] = df[[i]].astype('float')
    return df