| from __future__ import annotations |
|
|
| from typing import Any |
|
|
| from ..schemas.requests import ClinicalData |
|
|
|
|
| def build_feature_vector(data: ClinicalData) -> list[float]: |
| return [ |
| float(data.pregnancies), |
| float(data.glucose), |
| float(data.blood_pressure), |
| float(data.skin_thickness), |
| float(data.insulin), |
| float(data.bmi), |
| float(data.diabetes_pedigree_function), |
| float(data.age), |
| ] |
|
|
|
|
| def engineer_features(data: ClinicalData) -> dict[str, float | str]: |
| |
| |
| glucose_insulin_index = min(data.glucose / (data.insulin + 1.0), 10.0) |
| bmi_category = "normal" |
| if data.bmi >= 30: |
| bmi_category = "obese" |
| elif data.bmi >= 25: |
| bmi_category = "overweight" |
|
|
| if data.age < 35: |
| age_group = "young" |
| elif data.age < 55: |
| age_group = "adult" |
| else: |
| age_group = "senior" |
|
|
| glucose_bmi_index = (data.glucose / 100.0) * (data.bmi / 30.0) |
| age_risk_index = data.age / 50.0 |
| family_risk_index = data.diabetes_pedigree_function * 1.5 |
|
|
| return { |
| "glucose_insulin_index": round(glucose_insulin_index, 4), |
| "glucose_bmi_index": round(glucose_bmi_index, 4), |
| "age_risk_index": round(age_risk_index, 4), |
| "family_risk_index": round(family_risk_index, 4), |
| "bmi_category": bmi_category, |
| "age_group": age_group, |
| } |
|
|
|
|
| def risk_level_from_probability(probability: float) -> str: |
| if probability >= 0.7: |
| return "high" |
| if probability >= 0.4: |
| return "moderate" |
| return "low" |
|
|
|
|
| def normalize_scores(scores: dict[str, float]) -> dict[str, float]: |
| total = sum(max(score, 0.0) for score in scores.values()) |
| if total <= 0: |
| count = max(len(scores), 1) |
| return {label: 1.0 / count for label in scores} |
| return {label: max(score, 0.0) / total for label, score in scores.items()} |
|
|
|
|
| def symptoms_match_diabetes(text: str, top_label: str) -> bool: |
| lowered = text.lower() |
| diabetes_keywords = ["thirst", "urinate", "urination", "polyuria", "fatigue", "tired", "blurred", "vision", "weight loss"] |
| if top_label.lower().startswith("diabetes"): |
| return any(keyword in lowered for keyword in diabetes_keywords) |
| return False |
|
|
|
|
| def build_llm_context(clinical_data: ClinicalData, risk_probability: float, symptoms_text: str) -> dict[str, Any]: |
| return { |
| "clinical_data": { |
| "pregnancies": clinical_data.pregnancies, |
| "glucose": clinical_data.glucose, |
| "blood_pressure": clinical_data.blood_pressure, |
| "skin_thickness": clinical_data.skin_thickness, |
| "insulin": clinical_data.insulin, |
| "bmi": clinical_data.bmi, |
| "diabetes_pedigree_function": clinical_data.diabetes_pedigree_function, |
| "age": clinical_data.age, |
| }, |
| "risk_probability": round(risk_probability, 4), |
| "risk_level": risk_level_from_probability(risk_probability), |
| "symptoms_text": symptoms_text, |
| } |
|
|
