{"id":118,"date":"2023-12-01T05:37:40","date_gmt":"2023-12-01T05:37:40","guid":{"rendered":"https:\/\/drhack.gr\/?page_id=118"},"modified":"2023-12-01T05:48:16","modified_gmt":"2023-12-01T05:48:16","slug":"dwt","status":"publish","type":"page","link":"https:\/\/drhack.gr\/?page_id=118","title":{"rendered":"dwt"},"content":{"rendered":"\n<pre class=\"wp-block-code\"><code>\rDWT4\n\n\r\n\r\n\r\n\r\n\r\n# Categorical features encoded as integers\r\nECG = keras.Input(shape=(1,), name=\"ECG\", dtype=\"int64\")\r\n\r\n# Categorical feature encoded as string\r\nGROUP = keras.Input(shape=(1,), name=\"GROUP\", dtype=\"string\")\r\n\r\n# Numerical features\r\nDWTBPM1 = keras.Input(shape=(1,), name=\"DWTBPM1\")\r\nDWTBPM2 = keras.Input(shape=(1,), name=\"DWTBPM2\")\r\nDWTBPM3 = keras.Input(shape=(1,), name=\"DWTBPM3\")\r\nDWTBPM4 = keras.Input(shape=(1,), name=\"DWTBPM4\")\r\nDWTBPM5 = keras.Input(shape=(1,), name=\"DWTBPM5\")\r\nDWTBPM6 = keras.Input(shape=(1,), name=\"DWTBPM6\")\r\nDWTBPM7 = keras.Input(shape=(1,), name=\"DWTBPM7\")\r\nDWTBPM8 = keras.Input(shape=(1,), name=\"DWTBPM8\")\r\nDWTBPM9 = keras.Input(shape=(1,), name=\"DWTBPM9\")\r\nDWTBPM10 = keras.Input(shape=(1,), name=\"DWTBPM10\")\r\nDWTBPM11 = keras.Input(shape=(1,), name=\"DWTBPM11\")\r\nDWTBPM12 = keras.Input(shape=(1,), name=\"DWTBPM12\")\r\nDWTBPM13 = keras.Input(shape=(1,), name=\"DWTBPM13\")\r\nDWTBPM14 = keras.Input(shape=(1,), name=\"DWTBPM14\")\r\nDWTBPM15 = keras.Input(shape=(1,), name=\"DWTBPM15\")\r\nDWTBPM16 = keras.Input(shape=(1,), name=\"DWTBPM16\")\r\nDWTBPM17 = keras.Input(shape=(1,), name=\"DWTBPM17\")\r\nDWTBPM18 = keras.Input(shape=(1,), name=\"DWTBPM18\")\r\nDWTBPM19 = keras.Input(shape=(1,), name=\"DWTBPM19\")\r\nDWTBPM20 = keras.Input(shape=(1,), name=\"DWTBPM20\")\r\nDWTBPM21 = keras.Input(shape=(1,), name=\"DWTBPM21\")\r\nDWTBPM22 = keras.Input(shape=(1,), name=\"DWTBPM22\")\r\nDWTBPM23 = keras.Input(shape=(1,), name=\"DWTBPM23\")\r\nDWTBPM24 = keras.Input(shape=(1,), name=\"DWTBPM24\")\r\nDWTBPM25 = keras.Input(shape=(1,), name=\"DWTBPM25\")\r\nDWTBPM26 = keras.Input(shape=(1,), name=\"DWTBPM26\")\r\nDWTBPM27 = keras.Input(shape=(1,), name=\"DWTBPM27\")\r\nDWTBPM28 = keras.Input(shape=(1,), name=\"DWTBPM28\")\r\nDWTBPM29 = keras.Input(shape=(1,), name=\"DWTBPM29\")\r\nDWTBPM30 = keras.Input(shape=(1,), name=\"DWTBPM30\")\r\nDWTBPM31 = keras.Input(shape=(1,), name=\"DWTBPM31\")\r\nDWTBPM32 = keras.Input(shape=(1,), name=\"DWTBPM32\")\r\nDWTBPM33 = keras.Input(shape=(1,), name=\"DWTBPM33\")\r\nDWTBPM34 = keras.Input(shape=(1,), name=\"DWTBPM34\")\r\nDWTBPM35 = keras.Input(shape=(1,), name=\"DWTBPM35\")\r\nDWTBPM36 = keras.Input(shape=(1,), name=\"DWTBPM36\")\r\nDWTBPM37 = keras.Input(shape=(1,), name=\"DWTBPM37\")\r\nDWTBPM38 = keras.Input(shape=(1,), name=\"DWTBPM38\")\r\nDWTBPM39 = keras.Input(shape=(1,), name=\"DWTBPM39\")\r\nDWTBPM40 = keras.Input(shape=(1,), name=\"DWTBPM40\")\r\nDWTBPM41 = keras.Input(shape=(1,), name=\"DWTBPM41\")\r\nDWTBPM42 = keras.Input(shape=(1,), name=\"DWTBPM42\")\r\nDWTBPM43 = keras.Input(shape=(1,), name=\"DWTBPM43\")\r\nDWTBPM44 = keras.Input(shape=(1,), name=\"DWTBPM44\")\r\nDWTBPM45 = keras.Input(shape=(1,), name=\"DWTBPM45\")\r\nDWTBPM46 = keras.Input(shape=(1,), name=\"DWTBPM46\")\r\nDWTBPM47 = keras.Input(shape=(1,), name=\"DWTBPM47\")\r\nDWTBPM48 = keras.Input(shape=(1,), name=\"DWTBPM48\")\r\nDWTBPM49 = keras.Input(shape=(1,), name=\"DWTBPM49\")\r\nDWTBPM50 = keras.Input(shape=(1,), name=\"DWTBPM50\")\r\nDWTBPM51 = keras.Input(shape=(1,), name=\"DWTBPM51\")\r\nDWTBPM52 = keras.Input(shape=(1,), name=\"DWTBPM52\")\r\nDWTBPM53 = keras.Input(shape=(1,), name=\"DWTBPM53\")\r\nDWTBPM54 = keras.Input(shape=(1,), name=\"DWTBPM54\")\r\nDWTBPM55 = keras.Input(shape=(1,), name=\"DWTBPM55\")\r\nDWTBPM56 = keras.Input(shape=(1,), name=\"DWTBPM56\")\r\nDWTBPM57 = keras.Input(shape=(1,), name=\"DWTBPM57\")\r\nDWTBPM58 = keras.Input(shape=(1,), name=\"DWTBPM58\")\r\nDWTBPM59 = keras.Input(shape=(1,), name=\"DWTBPM59\")\r\nDWTBPM60 = keras.Input(shape=(1,), name=\"DWTBPM60\")\r\nDWTBPM61 = keras.Input(shape=(1,), name=\"DWTBPM61\")\r\nDWTBPM62 = keras.Input(shape=(1,), name=\"DWTBPM62\")\r\nDWTBPM63 = keras.Input(shape=(1,), name=\"DWTBPM63\")\r\nDWTBPM64 = keras.Input(shape=(1,), name=\"DWTBPM64\")\r\nDWTBPM65 = keras.Input(shape=(1,), name=\"DWTBPM65\")\r\nDWTBPM66 = keras.Input(shape=(1,), name=\"DWTBPM66\")\r\nDWTBPM67 = keras.Input(shape=(1,), name=\"DWTBPM67\")\r\nDWTBPM68 = keras.Input(shape=(1,), name=\"DWTBPM68\")\r\nDWTBPM69 = keras.Input(shape=(1,), name=\"DWTBPM69\")\r\nDWTBPM70 = keras.Input(shape=(1,), name=\"DWTBPM70\")\r\nDWTBPM71 = keras.Input(shape=(1,), name=\"DWTBPM71\")\r\nDWTBPM72 = keras.Input(shape=(1,), name=\"DWTBPM72\")\r\nDWTBPM73 = keras.Input(shape=(1,), name=\"DWTBPM73\")\r\nDWTBPM74 = keras.Input(shape=(1,), name=\"DWTBPM74\")\r\nDWTBPM75 = keras.Input(shape=(1,), name=\"DWTBPM75\")\r\nDWTBPM76 = keras.Input(shape=(1,), name=\"DWTBPM76\")\r\nDWTBPM77 = keras.Input(shape=(1,), name=\"DWTBPM77\")\r\nDWTBPM78 = keras.Input(shape=(1,), name=\"DWTBPM78\")\r\nDWTBPM79 = keras.Input(shape=(1,), name=\"DWTBPM79\")\r\nDWTBPM80 = keras.Input(shape=(1,), name=\"DWTBPM80\")\r\nDWTBPM81 = keras.Input(shape=(1,), name=\"DWTBPM81\")\r\nDWTBPM82 = keras.Input(shape=(1,), name=\"DWTBPM82\")\r\nDWTBPM83 = keras.Input(shape=(1,), name=\"DWTBPM83\")\r\nDWTBPM84 = keras.Input(shape=(1,), name=\"DWTBPM84\")\r\nDWTBPM85 = keras.Input(shape=(1,), name=\"DWTBPM85\")\r\nDWTBPM86 = keras.Input(shape=(1,), name=\"DWTBPM86\")\r\n\r\n\r\n\r\nall_inputs = &#91;\r\n    ECG,\r\n    GROUP,\r\n    DWTBPM1, \r\n    DWTBPM2, \r\n    DWTBPM3, \r\n    DWTBPM4, \r\n    DWTBPM5, \r\n    DWTBPM6, \r\n    DWTBPM7, \r\n    DWTBPM8, \r\n    DWTBPM9,\r\n    DWTBPM10,\r\n    DWTBPM11,\r\n    DWTBPM12,\r\n    DWTBPM13,\r\n    DWTBPM14,\r\n    DWTBPM15,\r\n    DWTBPM16,\r\n    DWTBPM17,\r\n    DWTBPM18,\r\n    DWTBPM19,\r\n    DWTBPM20,\r\n    DWTBPM21,\r\n    DWTBPM22,\r\n    DWTBPM23,\r\n    DWTBPM24,\r\n    DWTBPM25,\r\n    DWTBPM26,\r\n    DWTBPM27,\r\n    DWTBPM28,\r\n    DWTBPM29,\r\n    DWTBPM30,\r\n    DWTBPM31,\r\n    DWTBPM32,\r\n    DWTBPM33,\r\n    DWTBPM34,\r\n    DWTBPM35,\r\n    DWTBPM36,\r\n    DWTBPM37,\r\n    DWTBPM38,\r\n    DWTBPM39,\r\n    DWTBPM40,\r\n    DWTBPM41,\r\n    DWTBPM42,\r\n    DWTBPM43,\r\n    DWTBPM44,\r\n    DWTBPM45,\r\n    DWTBPM46,\r\n    DWTBPM47,\r\n    DWTBPM48,\r\n    DWTBPM49,\r\n    DWTBPM50,\r\n    DWTBPM51,\r\n    DWTBPM52,\r\n    DWTBPM53,\r\n    DWTBPM54,\r\n    DWTBPM55,\r\n    DWTBPM56,\r\n    DWTBPM57,\r\n    DWTBPM58,\r\n    DWTBPM59,\r\n    DWTBPM60,\r\n    DWTBPM61,\r\n    DWTBPM62,\r\n    DWTBPM63,\r\n    DWTBPM64,\r\n    DWTBPM65,\r\n    DWTBPM66,\r\n    DWTBPM67,\r\n    DWTBPM68,\r\n    DWTBPM69,\r\n    DWTBPM70,\r\n    DWTBPM71,\r\n    DWTBPM72,\r\n    DWTBPM73,\r\n    DWTBPM74,\r\n    DWTBPM75,\r\n    DWTBPM76,\r\n    DWTBPM77,\r\n    DWTBPM78,\r\n    DWTBPM79,\r\n    DWTBPM80,\r\n    DWTBPM81,\r\n    DWTBPM82,\r\n    DWTBPM83,\r\n    DWTBPM84,\r\n    DWTBPM85,\r\n    DWTBPM86,\r\n]\r\n\r\n# Integer categorical features\r\nECG_encoded = encode_categorical_feature(ECG, \"ECG\", train_ds, False)\r\n \r\n# String categorical features\r\nGROUP_encoded = encode_categorical_feature(GROUP, \"GROUP\", train_ds, True)\r\n\r\n# Numerical features\r\nDWTBPM1_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM1\", train_ds)\r\nDWTBPM2_encoded = encode_numerical_feature(DWTBPM2, \"DWTBPM2\", train_ds)\r\nDWTBPM3_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM3\", train_ds)\r\nDWTBPM4_encoded = encode_numerical_feature(DWTBPM4, \"DWTBPM4\", train_ds)\r\nDWTBPM5_encoded = encode_numerical_feature(DWTBPM5, \"DWTBPM5\", train_ds)\r\nDWTBPM6_encoded = encode_numerical_feature(DWTBPM6, \"DWTBPM6\", train_ds)\r\nDWTBPM7_encoded = encode_numerical_feature(DWTBPM7, \"DWTBPM7\", train_ds)\r\nDWTBPM8_encoded = encode_numerical_feature(DWTBPM8, \"DWTBPM8\", train_ds)\r\nDWTBPM9_encoded = encode_numerical_feature(DWTBPM9, \"DWTBPM9\", train_ds)\r\nDWTBPM10_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM1\", train_ds)\r\nDWTBPM11_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM11\", train_ds)\r\nDWTBPM12_encoded = encode_numerical_feature(DWTBPM2, \"DWTBPM12\", train_ds)\r\nDWTBPM13_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM13\", train_ds)\r\nDWTBPM14_encoded = encode_numerical_feature(DWTBPM4, \"DWTBPM14\", train_ds)\r\nDWTBPM15_encoded = encode_numerical_feature(DWTBPM5, \"DWTBPM15\", train_ds)\r\nDWTBPM16_encoded = encode_numerical_feature(DWTBPM6, \"DWTBPM16\", train_ds)\r\nDWTBPM17_encoded = encode_numerical_feature(DWTBPM7, \"DWTBPM17\", train_ds)\r\nDWTBPM18_encoded = encode_numerical_feature(DWTBPM8, \"DWTBPM18\", train_ds)\r\nDWTBPM19_encoded = encode_numerical_feature(DWTBPM9, \"DWTBPM19\", train_ds)\r\nDWTBPM20_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM20\", train_ds)\r\nDWTBPM21_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM21\", train_ds)\r\nDWTBPM22_encoded = encode_numerical_feature(DWTBPM2, \"DWTBPM22\", train_ds)\r\nDWTBPM23_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM23\", train_ds)\r\nDWTBPM24_encoded = encode_numerical_feature(DWTBPM4, \"DWTBPM24\", train_ds)\r\nDWTBPM25_encoded = encode_numerical_feature(DWTBPM5, \"DWTBPM25\", train_ds)\r\nDWTBPM26_encoded = encode_numerical_feature(DWTBPM6, \"DWTBPM26\", train_ds)\r\nDWTBPM27_encoded = encode_numerical_feature(DWTBPM7, \"DWTBPM27\", train_ds)\r\nDWTBPM28_encoded = encode_numerical_feature(DWTBPM8, \"DWTBPM28\", train_ds)\r\nDWTBPM29_encoded = encode_numerical_feature(DWTBPM9, \"DWTBPM29\", train_ds)\r\nDWTBPM30_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM30\", train_ds)\r\nDWTBPM31_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM31\", train_ds)\r\nDWTBPM32_encoded = encode_numerical_feature(DWTBPM2, \"DWTBPM32\", train_ds)\r\nDWTBPM33_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM33\", train_ds)\r\nDWTBPM34_encoded = encode_numerical_feature(DWTBPM4, \"DWTBPM34\", train_ds)\r\nDWTBPM35_encoded = encode_numerical_feature(DWTBPM5, \"DWTBPM35\", train_ds)\r\nDWTBPM36_encoded = encode_numerical_feature(DWTBPM6, \"DWTBPM36\", train_ds)\r\nDWTBPM37_encoded = encode_numerical_feature(DWTBPM7, \"DWTBPM37\", train_ds)\r\nDWTBPM38_encoded = encode_numerical_feature(DWTBPM8, \"DWTBPM38\", train_ds)\r\nDWTBPM39_encoded = encode_numerical_feature(DWTBPM9, \"DWTBPM39\", train_ds)\r\nDWTBPM40_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM40\", train_ds)\r\nDWTBPM41_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM41\", train_ds)\r\nDWTBPM42_encoded = encode_numerical_feature(DWTBPM2, \"DWTBPM42\", train_ds)\r\nDWTBPM43_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM43\", train_ds)\r\nDWTBPM44_encoded = encode_numerical_feature(DWTBPM4, \"DWTBPM44\", train_ds)\r\nDWTBPM45_encoded = encode_numerical_feature(DWTBPM5, \"DWTBPM45\", train_ds)\r\nDWTBPM46_encoded = encode_numerical_feature(DWTBPM6, \"DWTBPM46\", train_ds)\r\nDWTBPM47_encoded = encode_numerical_feature(DWTBPM7, \"DWTBPM47\", train_ds)\r\nDWTBPM48_encoded = encode_numerical_feature(DWTBPM8, \"DWTBPM48\", train_ds)\r\nDWTBPM49_encoded = encode_numerical_feature(DWTBPM9, \"DWTBPM49\", train_ds)\r\nDWTBPM50_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM50\", train_ds)\r\nDWTBPM51_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM51\", train_ds)\r\nDWTBPM52_encoded = encode_numerical_feature(DWTBPM2, \"DWTBPM52\", train_ds)\r\nDWTBPM53_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM53\", train_ds)\r\nDWTBPM54_encoded = encode_numerical_feature(DWTBPM4, \"DWTBPM54\", train_ds)\r\nDWTBPM55_encoded = encode_numerical_feature(DWTBPM5, \"DWTBPM55\", train_ds)\r\nDWTBPM56_encoded = encode_numerical_feature(DWTBPM6, \"DWTBPM56\", train_ds)\r\nDWTBPM57_encoded = encode_numerical_feature(DWTBPM7, \"DWTBPM57\", train_ds)\r\nDWTBPM58_encoded = encode_numerical_feature(DWTBPM8, \"DWTBPM58\", train_ds)\r\nDWTBPM59_encoded = encode_numerical_feature(DWTBPM9, \"DWTBPM59\", train_ds)\r\nDWTBPM60_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM60\", train_ds)\r\nDWTBPM61_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM61\", train_ds)\r\nDWTBPM62_encoded = encode_numerical_feature(DWTBPM2, \"DWTBPM62\", train_ds)\r\nDWTBPM63_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM63\", train_ds)\r\nDWTBPM64_encoded = encode_numerical_feature(DWTBPM4, \"DWTBPM64\", train_ds)\r\nDWTBPM65_encoded = encode_numerical_feature(DWTBPM5, \"DWTBPM65\", train_ds)\r\nDWTBPM66_encoded = encode_numerical_feature(DWTBPM6, \"DWTBPM66\", train_ds)\r\nDWTBPM67_encoded = encode_numerical_feature(DWTBPM7, \"DWTBPM67\", train_ds)\r\nDWTBPM68_encoded = encode_numerical_feature(DWTBPM8, \"DWTBPM68\", train_ds)\r\nDWTBPM69_encoded = encode_numerical_feature(DWTBPM9, \"DWTBPM69\", train_ds)\r\nDWTBPM70_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM70\", train_ds)\r\nDWTBPM71_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM71\", train_ds)\r\nDWTBPM72_encoded = encode_numerical_feature(DWTBPM2, \"DWTBPM72\", train_ds)\r\nDWTBPM73_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM73\", train_ds)\r\nDWTBPM74_encoded = encode_numerical_feature(DWTBPM4, \"DWTBPM74\", train_ds)\r\nDWTBPM75_encoded = encode_numerical_feature(DWTBPM5, \"DWTBPM75\", train_ds)\r\nDWTBPM76_encoded = encode_numerical_feature(DWTBPM6, \"DWTBPM76\", train_ds)\r\nDWTBPM77_encoded = encode_numerical_feature(DWTBPM7, \"DWTBPM77\", train_ds)\r\nDWTBPM78_encoded = encode_numerical_feature(DWTBPM8, \"DWTBPM78\", train_ds)\r\nDWTBPM79_encoded = encode_numerical_feature(DWTBPM9, \"DWTBPM79\", train_ds)\r\nDWTBPM80_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM80\", train_ds)\r\nDWTBPM81_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM81\", train_ds)\r\nDWTBPM82_encoded = encode_numerical_feature(DWTBPM2, \"DWTBPM82\", train_ds)\r\nDWTBPM83_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM83\", train_ds)\r\nDWTBPM84_encoded = encode_numerical_feature(DWTBPM4, \"DWTBPM84\", train_ds)\r\nDWTBPM85_encoded = encode_numerical_feature(DWTBPM5, \"DWTBPM85\", train_ds)\r\nDWTBPM86_encoded = encode_numerical_feature(DWTBPM6, \"DWTBPM86\", train_ds)\r\n\r\n\r\nall_features = layers.concatenate(\r\n    &#91;\r\n        ECG_encoded,\r\n        GROUP_encoded,\r\n        DWTBPM1_encoded,\r\n        DWTBPM2_encoded,\r\n        DWTBPM3_encoded,\r\n        DWTBPM4_encoded,\r\n        DWTBPM5_encoded,\r\n        DWTBPM6_encoded,\r\n        DWTBPM7_encoded,\r\n        DWTBPM8_encoded,\r\n        DWTBPM9_encoded,\r\n        DWTBPM10_encoded,\r\n        DWTBPM11_encoded,\r\n        DWTBPM12_encoded,\r\n        DWTBPM13_encoded,\r\n        DWTBPM14_encoded,\r\n        DWTBPM15_encoded,\r\n        DWTBPM16_encoded,\r\n        DWTBPM17_encoded,\r\n        DWTBPM18_encoded,\r\n        DWTBPM19_encoded,\r\n        DWTBPM20_encoded,\r\n        DWTBPM21_encoded,\r\n        DWTBPM22_encoded,\r\n        DWTBPM23_encoded,\r\n        DWTBPM24_encoded,\r\n        DWTBPM25_encoded,\r\n        DWTBPM26_encoded,\r\n        DWTBPM27_encoded,\r\n        DWTBPM28_encoded,\r\n        DWTBPM29_encoded,\r\n        DWTBPM30_encoded,\r\n        DWTBPM31_encoded,\r\n        DWTBPM32_encoded,\r\n        DWTBPM33_encoded,\r\n        DWTBPM34_encoded,\r\n        DWTBPM35_encoded,\r\n        DWTBPM36_encoded,\r\n        DWTBPM37_encoded,\r\n        DWTBPM38_encoded,\r\n        DWTBPM39_encoded,\r\n        DWTBPM40_encoded,\r\n        DWTBPM41_encoded,\r\n        DWTBPM42_encoded,\r\n        DWTBPM43_encoded,\r\n        DWTBPM44_encoded,\r\n        DWTBPM45_encoded,\r\n        DWTBPM46_encoded,\r\n        DWTBPM47_encoded,\r\n        DWTBPM48_encoded,\r\n        DWTBPM49_encoded,\r\n        DWTBPM50_encoded,\r\n        DWTBPM51_encoded,\r\n        DWTBPM52_encoded,\r\n        DWTBPM53_encoded,\r\n        DWTBPM54_encoded,\r\n        DWTBPM55_encoded,\r\n        DWTBPM56_encoded,\r\n        DWTBPM57_encoded,\r\n        DWTBPM58_encoded,\r\n        DWTBPM59_encoded,\r\n        DWTBPM60_encoded,\r\n        DWTBPM61_encoded,\r\n        DWTBPM62_encoded,\r\n        DWTBPM63_encoded,\r\n        DWTBPM64_encoded,\r\n        DWTBPM65_encoded,\r\n        DWTBPM66_encoded,\r\n        DWTBPM67_encoded,\r\n        DWTBPM68_encoded,\r\n        DWTBPM69_encoded,\r\n        DWTBPM70_encoded,\r\n        DWTBPM71_encoded,\r\n        DWTBPM72_encoded,\r\n        DWTBPM73_encoded,\r\n        DWTBPM74_encoded,\r\n        DWTBPM75_encoded,\r\n        DWTBPM76_encoded,\r\n        DWTBPM77_encoded,\r\n        DWTBPM78_encoded,\r\n        DWTBPM79_encoded,\r\n        DWTBPM80_encoded,\r\n        DWTBPM81_encoded,\r\n        DWTBPM82_encoded,\r\n        DWTBPM83_encoded,\r\n        DWTBPM84_encoded,\r\n        DWTBPM85_encoded,\r\n        DWTBPM86_encoded,\r\n    ]\r\n)\r\nx = layers.Dense(32, activation=\"relu\")(all_features)\r\nx = layers.Dropout(0.5)(x)\r\noutput = layers.Dense(1, activation=\"sigmoid\")(x)\r\nmodel = keras.Model(all_inputs, output)\r\nmodel.compile(\"adam\", \"binary_crossentropy\", metrics=&#91;\"accuracy\"])\r\n\r\n\r\n\nsample = {\r\n    \"ECG\": 0,\r\n    \"GROUP\":\"T\", \r\n    \"DWTBPM1\":15.049,\r\n    \"DWTBPM2\":15.954,\r\n    \"DWTBPM3\":0.71133,\r\n    \"DWTBPM4\":-0.16943,\r\n    \"DWTBPM5\":-0.093617,\r\n    \"DWTBPM6\":-0.3758,\r\n    \"DWTBPM7\":0.4191,\r\n    \"DWTBPM8\":-0.27348,\r\n    \"DWTBPM9\":0.51794,\r\n    \"DWTBPM10\":0.7778,\r\n    \"DWTBPM11\":-0.37705,\r\n    \"DWTBPM12\":-0.43298,\r\n    \"DWTBPM13\":0.49203,\r\n    \"DWTBPM14\":-0.50637,\r\n    \"DWTBPM15\":10.312,\r\n    \"DWTBPM16\":0.11405,\r\n    \"DWTBPM17\":0.87534,\r\n    \"DWTBPM18\":-0.41432,\r\n    \"DWTBPM19\":0.93978,\r\n    \"DWTBPM20\":0.11845,\r\n    \"DWTBPM21\":-0.18828,\r\n    \"DWTBPM22\":-0.2243,\r\n    \"DWTBPM23\":-0.16092,\r\n    \"DWTBPM24\":-0.16067,\r\n    \"DWTBPM25\":-0.17874,\r\n    \"DWTBPM26\":-0.35341,\r\n    \"DWTBPM27\":0.82711,\r\n    \"DWTBPM28\":-0.35977,\r\n    \"DWTBPM29\":0.80043,\r\n    \"DWTBPM30\":0.41024,\r\n    \"DWTBPM31\":-0.0053516,\r\n    \"DWTBPM32\":-0.00056808,\r\n    \"DWTBPM33\":-0.13293,\r\n    \"DWTBPM34\":-0.19591,\r\n    \"DWTBPM35\":-0.25608,\r\n    \"DWTBPM36\":0.73422,\r\n    \"DWTBPM37\":-0.29908,\r\n    \"DWTBPM38\":10.422,\r\n    \"DWTBPM39\":0.31114,\r\n    \"DWTBPM40\":-0.16143,\r\n    \"DWTBPM41\":-0.287,\r\n    \"DWTBPM42\":0.33273,\r\n    \"DWTBPM43\":-0.05755,\r\n    \"DWTBPM44\":0.42025,\r\n    \"DWTBPM45\":10.731,\r\n    \"DWTBPM46\":-0.4099,\r\n    \"DWTBPM47\":-0.18027,\r\n    \"DWTBPM48\":-0.24305,\r\n    \"DWTBPM49\":-0.46275,\r\n    \"DWTBPM50\":0.57248,\r\n    \"DWTBPM51\":-0.21145,\r\n    \"DWTBPM52\":0.44987,\r\n    \"DWTBPM53\":0.78692,\r\n    \"DWTBPM54\":-0.31844,\r\n    \"DWTBPM55\":-0.23371,\r\n    \"DWTBPM56\":-0.052245,\r\n    \"DWTBPM57\":0.15358,\r\n    \"DWTBPM58\":-0.066129,\r\n    \"DWTBPM59\":13.377,\r\n    \"DWTBPM60\":-0.28406,\r\n    \"DWTBPM61\":-0.18193,\r\n    \"DWTBPM62\":-0.38386,\r\n    \"DWTBPM63\":-0.18971,\r\n    \"DWTBPM64\":0.42986,\r\n    \"DWTBPM65\":-0.30491,\r\n    \"DWTBPM66\":12.464,\r\n    \"DWTBPM67\":-0.11152,\r\n    \"DWTBPM68\":-0.18534,\r\n    \"DWTBPM69\":-0.38486,\r\n    \"DWTBPM70\":0.23502,\r\n    \"DWTBPM71\":-0.11435,\r\n    \"DWTBPM72\":0.31003,\r\n    \"DWTBPM73\":0.93679,\r\n    \"DWTBPM74\":-0.43053,\r\n    \"DWTBPM75\":-0.20893,\r\n    \"DWTBPM76\":-0.45197,\r\n    \"DWTBPM77\":0.40383,\r\n    \"DWTBPM78\":-0.29302,\r\n    \"DWTBPM79\":0.5754,\r\n    \"DWTBPM80\":0.68984,\r\n    \"DWTBPM81\":-0.3486,\r\n    \"DWTBPM82\":-0.1586,\r\n    \"DWTBPM83\":-0.37793,\r\n    \"DWTBPM84\":-0.36735,\r\n    \"DWTBPM85\":-0.37782,\r\n    \"DWTBPM86\":-0.37729,\r\n \r\n    }\r\n\r\ninput_dict = {name: tf.convert_to_tensor(&#91;value]) for name, value in sample.items()}\r\npredictions = model.predict(input_dict)\r\n\r\nprint(\r\n    \"This particular patient had a %.1f percent probability \"\r\n    \"of having a heart disease, as evaluated by our model.\" % (100 * predictions&#91;0]&#91;0],)\r\n)\r\n\r\n\r\n\n\n\n\r\n<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>DWT9\n\r\n\r\n# Categorical features encoded as integers\r\nECG = keras.Input(shape=(1,), name=\"ECG\", dtype=\"int64\")\r\n\r\n# Categorical feature encoded as string\r\nGROUP = keras.Input(shape=(1,), name=\"GROUP\", dtype=\"string\")\r\n\r\n# Numerical features\r\nDWTBPM1 = keras.Input(shape=(1,), name=\"DWTBPM1\")\r\nDWTBPM2 = keras.Input(shape=(1,), name=\"DWTBPM2\")\r\nDWTBPM3 = keras.Input(shape=(1,), name=\"DWTBPM3\")\r\nDWTBPM4 = keras.Input(shape=(1,), name=\"DWTBPM4\")\r\nDWTBPM5 = keras.Input(shape=(1,), name=\"DWTBPM5\")\r\nDWTBPM6 = keras.Input(shape=(1,), name=\"DWTBPM6\")\r\nDWTBPM7 = keras.Input(shape=(1,), name=\"DWTBPM7\")\r\nDWTBPM8 = keras.Input(shape=(1,), name=\"DWTBPM8\")\r\nDWTBPM9 = keras.Input(shape=(1,), name=\"DWTBPM9\")\r\n\r\nall_inputs = &#91;\r\n    ECG,\r\n    GROUP,\r\n    DWTBPM1, \r\n    DWTBPM2, \r\n    DWTBPM3, \r\n    DWTBPM4, \r\n    DWTBPM5, \r\n    DWTBPM6, \r\n    DWTBPM7, \r\n    DWTBPM8, \r\n    DWTBPM9,\r\n]\r\n\r\n# Integer categorical features\r\nECG_encoded = encode_categorical_feature(ECG, \"ECG\", train_ds, False)\r\n \r\n# String categorical features\r\nGROUP_encoded = encode_categorical_feature(GROUP, \"GROUP\", train_ds, True)\r\n\r\n# Numerical features\r\nDWTBPM1_encoded = encode_numerical_feature(DWTBPM1, \"DWTBPM1\", train_ds)\r\nDWTBPM2_encoded = encode_numerical_feature(DWTBPM2, \"DWTBPM2\", train_ds)\r\nDWTBPM3_encoded = encode_numerical_feature(DWTBPM3, \"DWTBPM3\", train_ds)\r\nDWTBPM4_encoded = encode_numerical_feature(DWTBPM4, \"DWTBPM4\", train_ds)\r\nDWTBPM5_encoded = encode_numerical_feature(DWTBPM5, \"DWTBPM5\", train_ds)\r\nDWTBPM6_encoded = encode_numerical_feature(DWTBPM6, \"DWTBPM6\", train_ds)\r\nDWTBPM7_encoded = encode_numerical_feature(DWTBPM7, \"DWTBPM7\", train_ds)\r\nDWTBPM8_encoded = encode_numerical_feature(DWTBPM8, \"DWTBPM8\", train_ds)\r\nDWTBPM9_encoded = encode_numerical_feature(DWTBPM9, \"DWTBPM9\", train_ds)\r\n\r\nall_features = layers.concatenate(\r\n    &#91;\r\n        ECG_encoded,\r\n        GROUP_encoded,\r\n        DWTBPM1_encoded,\r\n        DWTBPM2_encoded,\r\n        DWTBPM3_encoded,\r\n        DWTBPM4_encoded,\r\n        DWTBPM5_encoded,\r\n        DWTBPM6_encoded,\r\n        DWTBPM7_encoded,\r\n        DWTBPM8_encoded,\r\n        DWTBPM9_encoded,\r\n    ]\r\n)\r\nx = layers.Dense(32, activation=\"relu\")(all_features)\r\nx = layers.Dropout(0.5)(x)\r\noutput = layers.Dense(1, activation=\"sigmoid\")(x)\r\nmodel = keras.Model(all_inputs, output)\r\nmodel.compile(\"adam\", \"binary_crossentropy\", metrics=&#91;\"accuracy\"])<\/code><\/pre>\n\n\n\n<pre class=\"wp-block-code\"><code>\r\n\r\n\r\n\r\n\r\nsample = {\r\n    \"ECG\": 0,\r\n    \"GROUP\":\"T\",\r\n    \"DWTBPM1\": -5.20770,\t\t\t\t\t\t\t\r\n    \"DWTBPM2\": -8.77470,\r\n    \"DWTBPM3\": 6.07780,\r\n    \"DWTBPM4\": 6.33166,\r\n    \"DWTBPM5\": 0.36817,\r\n    \"DWTBPM6\": 0.21152,\r\n    \"DWTBPM7\": 0.13017,\r\n    \"DWTBPM8\": 0.22935,\r\n    \"DWTBPM9\": -1.12358,\r\n    }\r\n\r\ninput_dict = {name: tf.convert_to_tensor(&#91;value]) for name, value in sample.items()}\r\npredictions = model.predict(input_dict)\r\n\r\nprint(\r\n    \"This particular patient had a %.1f percent probability \"\r\n    \"of having a heart disease, as evaluated by our model.\" % (100 * predictions&#91;0]&#91;0],)\r\n<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-118","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/drhack.gr\/index.php?rest_route=\/wp\/v2\/pages\/118","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/drhack.gr\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/drhack.gr\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/drhack.gr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/drhack.gr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=118"}],"version-history":[{"count":3,"href":"https:\/\/drhack.gr\/index.php?rest_route=\/wp\/v2\/pages\/118\/revisions"}],"predecessor-version":[{"id":122,"href":"https:\/\/drhack.gr\/index.php?rest_route=\/wp\/v2\/pages\/118\/revisions\/122"}],"wp:attachment":[{"href":"https:\/\/drhack.gr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=118"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}