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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.feature_selection import mutual_info_classif\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"data = pd.read_csv('pcos_cleaned.csv')\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"y = data[\"PCOS (Y/N)\"]\n",
"X = data.drop([\"PCOS (Y/N)\"], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Feature Mutual Information\n",
"38 Follicle No. (R) 0.240107\n",
"37 Follicle No. (L) 0.198132\n",
"33 Fast food (Y/N) 0.095965\n",
"29 hair growth(Y/N) 0.094711\n",
"30 Skin darkening (Y/N) 0.094472\n",
"28 Weight gain(Y/N) 0.091420\n",
"10 Cycle length(days) 0.074662\n",
"23 AMH(ng/mL) 0.066603\n",
"18 FSH/LH 0.065068\n",
"24 PRL(ng/mL) 0.061647\n",
"9 Cycle(R/I) 0.052702\n",
"13 No. of abortions 0.028979\n",
"7 RR (breaths/min) 0.028374\n",
"20 Waist(inch) 0.026092\n",
"12 Pregnant(Y/N) 0.024060\n",
"32 Pimples(Y/N) 0.023784\n",
"39 Avg. F size (L) (mm) 0.022989\n",
"22 TSH (mIU/L) 0.022234\n",
"31 Hair loss(Y/N) 0.019978\n",
"40 Avg. F size (R) (mm) 0.019886\n",
"16 FSH(mIU/mL) 0.019688\n",
"1 Age (yrs) 0.019323\n",
"0 Unnamed: 0 0.017659\n",
"17 LH(mIU/mL) 0.017577\n",
"4 BMI 0.015053\n",
"25 Vit D3 (ng/mL) 0.014276\n",
"6 Pulse rate(bpm) 0.013627\n",
"34 Reg.Exercise(Y/N) 0.009540\n",
"36 BP _Diastolic (mmHg) 0.008657\n",
"14 I beta-HCG(mIU/mL) 0.007298\n",
"35 BP _Systolic (mmHg) 0.006151\n",
"41 Endometrium (mm) 0.004497\n",
"3 Height(Cm) 0.000000\n",
"5 Blood Group 0.000000\n",
"8 Hb(g/dl) 0.000000\n",
"2 Weight (Kg) 0.000000\n",
"11 Marraige Status (Yrs) 0.000000\n",
"15 II beta-HCG(mIU/mL) 0.000000\n",
"19 Hip(inch) 0.000000\n",
"21 Waist:Hip Ratio 0.000000\n",
"27 RBS(mg/dl) 0.000000\n",
"26 PRG(ng/mL) 0.000000\n"
]
}
],
"source": [
"# Calculate Mutual Information\n",
"mi = mutual_info_classif(X, y)\n",
"\n",
"# Create a DataFrame to show feature importance\n",
"mi_df = pd.DataFrame({'Feature': X.columns, 'Mutual Information': mi})\n",
"\n",
"# Sort features by mutual information value\n",
"mi_df = mi_df.sort_values(by='Mutual Information', ascending=False)\n",
"\n",
"print(mi_df)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" PCOS (Y/N) Follicle No. (R) Follicle No. (L) Skin darkening (Y/N) \\\n",
"0 0 3 3 0 \n",
"1 0 5 3 0 \n",
"2 1 15 13 0 \n",
"3 0 2 2 0 \n",
"4 0 4 3 0 \n",
"\n",
" hair growth(Y/N) Weight gain(Y/N) Cycle length(days) AMH(ng/mL) \\\n",
"0 0 0 5 2.07 \n",
"1 0 0 5 1.53 \n",
"2 0 0 5 6.63 \n",
"3 0 0 5 1.22 \n",
"4 0 0 5 2.26 \n",
"\n",
" Fast food (Y/N) Cycle(R/I) FSH/LH PRL(ng/mL) Pimples(Y/N) Age (yrs) \\\n",
"0 1.0 0 2.160326 45.16 0 28 \n",
"1 0.0 0 6.174312 20.09 0 36 \n",
"2 1.0 0 6.295455 10.52 1 33 \n",
"3 0.0 0 3.415254 36.90 0 37 \n",
"4 0.0 0 4.422222 30.09 0 25 \n",
"\n",
" BMI \n",
"0 19.3 \n",
"1 24.9 \n",
"2 25.3 \n",
"3 29.7 \n",
"4 20.1 \n"
]
}
],
"source": [
"pcos_df = pd.read_csv('pcos_cleaned.csv')\n",
"pcos_df.columns = pcos_df.columns.str.strip() # Remove any leading/trailing whitespace\n",
"selected_features = ['PCOS (Y/N)', 'Follicle No. (R)', 'Follicle No. (L)', \n",
" 'Skin darkening (Y/N)', 'hair growth(Y/N)', 'Weight gain(Y/N)', \n",
" 'Cycle length(days)', 'AMH(ng/mL)', 'Fast food (Y/N)', \n",
" 'Cycle(R/I)', 'FSH/LH', 'PRL(ng/mL)', 'Pimples(Y/N)', \n",
" 'Age (yrs)', 'BMI']\n",
"\n",
"new_dataset = pcos_df[selected_features]\n",
"\n",
"# Save the new dataset to a CSV file if needed\n",
"new_dataset.to_csv('new_pcos_dataset.csv', index=False)\n",
"\n",
"# Display the new dataset\n",
"print(new_dataset.head())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"display_name": "Python 3",
"language": "python",
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