💖💖作者：计算机毕业设计江挽 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
基于大数据的孕产妇健康风险数据分析系统介绍
《孕产妇健康风险数据分析系统》是一套基于Hadoop+Spark大数据双引擎架构的智能健康风险评估平台，专门针对孕产妇群体的健康数据进行深度挖掘与分析。系统采用Python作为主要开发语言，结合Django框架构建稳定的后端服务体系，前端采用Vue+ElementUI+Echarts技术栈实现现代化的用户交互界面和数据可视化展示。通过Hadoop分布式文件系统（HDFS）实现海量孕产妇健康数据的存储管理，利用Spark及Spark SQL引擎进行高效的数据处理与分析计算，结合Pandas、NumPy等数据科学工具库完成复杂的统计分析任务。系统核心功能涵盖基础健康状况分析、心血管风险评估、代谢健康监测、高风险人群识别以及临床预警系统等多个维度，通过多层次的数据挖掘算法和风险评估模型，为医疗机构提供科学的决策支持，帮助医护人员及时发现潜在健康风险，制定个性化的健康管理方案，从而提升孕产妇及胎儿的健康保障水平。
基于大数据的孕产妇健康风险数据分析系统演示视频
基于大数据的孕产妇健康风险数据分析系统演示图片
基于大数据的孕产妇健康风险数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.stat import Correlation
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
def analyze_basic_health_status(maternal_data):
spark = SparkSession.builder.appName("BasicHealthAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
df = spark.createDataFrame(maternal_data)
age_stats = df.select(avg("age").alias("avg_age"), stddev("age").alias("std_age"), min("age").alias("min_age"), max("age").alias("max_age"))
bmi_distribution = df.groupBy(when(col("bmi") < 18.5, "偏瘦").when((col("bmi") >= 18.5) & (col("bmi") < 24), "正常").when((col("bmi") >= 24) & (col("bmi") < 28), "偏胖").otherwise("肥胖").alias("bmi_category")).count()
blood_pressure_analysis = df.select(avg("systolic_bp").alias("avg_systolic"), avg("diastolic_bp").alias("avg_diastolic"), count(when(col("systolic_bp") > 140, True)).alias("high_bp_count"))
weight_gain_trend = df.select(col("patient_id"), col("gestational_week"), col("weight"), lag("weight").over(Window.partitionBy("patient_id").orderBy("gestational_week")).alias("prev_weight"))
weight_gain_trend = weight_gain_trend.withColumn("weight_gain", col("weight") - col("prev_weight"))
abnormal_indicators = df.select(count(when(col("hemoglobin") < 110, True)).alias("anemia_count"), count(when(col("glucose_fasting") > 5.1, True)).alias("high_glucose_count"), count(when(col("protein_urine") > 0, True)).alias("proteinuria_count"))
health_score = df.withColumn("health_score", when((col("bmi") >= 18.5) & (col("bmi") < 28) & (col("systolic_bp") < 140) & (col("hemoglobin") >= 110), 100).when((col("bmi") < 18.5) | (col("bmi") >= 28) | (col("systolic_bp") >= 140) | (col("hemoglobin") < 110), 70).otherwise(50))
gestational_age_analysis = df.groupBy("gestational_week").agg(avg("weight").alias("avg_weight"), avg("bmi").alias("avg_bmi"), count("*").alias("patient_count"))
result_summary = df.agg(count("*").alias("total_patients"), avg("age").alias("average_age"), countDistinct("patient_id").alias("unique_patients"))
return {"age_statistics": age_stats.collect(), "bmi_distribution": bmi_distribution.collect(), "blood_pressure_analysis": blood_pressure_analysis.collect(), "weight_gain_analysis": weight_gain_trend.filter(col("weight_gain").isNotNull()).collect(), "abnormal_indicators": abnormal_indicators.collect(), "health_scores": health_score.collect(), "gestational_analysis": gestational_age_analysis.collect(), "summary": result_summary.collect()}
def analyze_cardiovascular_risk(maternal_data):
spark = SparkSession.builder.appName("CardiovascularRiskAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
df = spark.createDataFrame(maternal_data)
hypertension_risk = df.withColumn("hypertension_risk", when((col("systolic_bp") >= 140) | (col("diastolic_bp") >= 90), "高风险").when((col("systolic_bp") >= 130) | (col("diastolic_bp") >= 85), "中风险").otherwise("低风险"))
preeclampsia_risk = df.withColumn("preeclampsia_risk", when((col("systolic_bp") >= 140) & (col("protein_urine") > 0) & (col("gestational_week") >= 20), "高风险").when((col("systolic_bp") >= 130) | (col("protein_urine") > 0), "中风险").otherwise("低风险"))
cardiac_workload = df.withColumn("cardiac_workload", col("systolic_bp") * col("heart_rate") / 100)
blood_flow_analysis = df.withColumn("pulse_pressure", col("systolic_bp") - col("diastolic_bp")).withColumn("mean_arterial_pressure", (col("systolic_bp") + 2 * col("diastolic_bp")) / 3)
risk_factors_score = df.withColumn("cv_risk_score", when(col("age") > 35, 2).otherwise(0) + when(col("bmi") > 30, 2).otherwise(0) + when(col("systolic_bp") > 140, 3).otherwise(0) + when(col("diabetes_history") == 1, 2).otherwise(0) + when(col("family_cv_history") == 1, 1).otherwise(0))
high_risk_patients = df.filter((col("systolic_bp") >= 160) | (col("diastolic_bp") >= 110) | (col("cv_risk_score") >= 6))
bp_trend_analysis = df.select(col("patient_id"), col("visit_date"), col("systolic_bp"), lag("systolic_bp").over(Window.partitionBy("patient_id").orderBy("visit_date")).alias("prev_systolic"))
bp_trend_analysis = bp_trend_analysis.withColumn("bp_change", col("systolic_bp") - col("prev_systolic")).withColumn("bp_trend", when(col("bp_change") > 10, "上升").when(col("bp_change") < -10, "下降").otherwise("稳定"))
gestational_cv_risk = df.groupBy("gestational_week").agg(avg("systolic_bp").alias("avg_systolic"), avg("diastolic_bp").alias("avg_diastolic"), count(when(col("systolic_bp") >= 140, True)).alias("hypertension_count"))
medication_effect = df.filter(col("medication") == 1).select(avg("systolic_bp").alias("medicated_avg_bp"), count("*").alias("medicated_count"))
return {"hypertension_analysis": hypertension_risk.groupBy("hypertension_risk").count().collect(), "preeclampsia_analysis": preeclampsia_risk.groupBy("preeclampsia_risk").count().collect(), "cardiac_workload": cardiac_workload.select(avg("cardiac_workload"), max("cardiac_workload")).collect(), "blood_flow_metrics": blood_flow_analysis.select(avg("pulse_pressure"), avg("mean_arterial_pressure")).collect(), "risk_score_distribution": risk_factors_score.select(avg("cv_risk_score"), max("cv_risk_score")).collect(), "high_risk_patients": high_risk_patients.count(), "bp_trend_analysis": bp_trend_analysis.filter(col("bp_change").isNotNull()).collect(), "gestational_risk": gestational_cv_risk.collect()}
def identify_high_risk_population(maternal_data):
spark = SparkSession.builder.appName("HighRiskIdentification").config("spark.sql.adaptive.enabled", "true").getOrCreate()
df = spark.createDataFrame(maternal_data)
maternal_age_risk = df.withColumn("age_risk_level", when(col("age") >= 40, "极高风险").when(col("age") >= 35, "高风险").when(col("age") < 18, "高风险").otherwise("正常风险"))
multiple_pregnancy_risk = df.withColumn("pregnancy_risk", when(col("twin_pregnancy") == 1, "高风险").when(col("pregnancy_count") > 3, "中风险").otherwise("正常风险"))
medical_history_risk = df.withColumn("history_risk_score", when(col("diabetes_history") == 1, 3).otherwise(0) + when(col("hypertension_history") == 1, 3).otherwise(0) + when(col("heart_disease_history") == 1, 4).otherwise(0) + when(col("kidney_disease_history") == 1, 2).otherwise(0) + when(col("previous_pregnancy_complications") == 1, 2).otherwise(0))
gestational_complications = df.withColumn("gestational_risk", when((col("gestational_diabetes") == 1) | (col("gestational_hypertension") == 1), "高风险").when(col("anemia") == 1, "中风险").otherwise("正常"))
composite_risk_score = df.withColumn("total_risk_score", when(col("age") >= 35, 2).otherwise(0) + when(col("bmi") > 30, 2).otherwise(0) + col("history_risk_score") + when(col("systolic_bp") > 140, 3).otherwise(0) + when(col("gestational_diabetes") == 1, 2).otherwise(0) + when(col("twin_pregnancy") == 1, 2).otherwise(0))
high_risk_classification = composite_risk_score.withColumn("final_risk_level", when(col("total_risk_score") >= 8, "极高风险").when(col("total_risk_score") >= 5, "高风险").when(col("total_risk_score") >= 3, "中风险").otherwise("低风险"))
risk_factor_analysis = df.groupBy("gestational_week").agg(count(when(col("total_risk_score") >= 5, True)).alias("high_risk_count"), count("*").alias("total_patients"), (count(when(col("total_risk_score") >= 5, True)) * 100.0 / count("*")).alias("high_risk_percentage"))
urgent_intervention_cases = df.filter((col("total_risk_score") >= 8) | (col("systolic_bp") >= 160) | ((col("gestational_diabetes") == 1) & (col("glucose_fasting") > 7.0)))
risk_trend_monitoring = df.select(col("patient_id"), col("visit_date"), col("total_risk_score"), lag("total_risk_score").over(Window.partitionBy("patient_id").orderBy("visit_date")).alias("prev_risk_score"))
risk_trend_monitoring = risk_trend_monitoring.withColumn("risk_change", col("total_risk_score") - col("prev_risk_score")).withColumn("risk_trend", when(col("risk_change") > 2, "风险上升").when(col("risk_change") < -2, "风险下降").otherwise("风险稳定"))
specialized_care_recommendations = high_risk_classification.withColumn("care_recommendation", when(col("final_risk_level") == "极高风险", "立即转诊专科").when(col("final_risk_level") == "高风险", "增加监测频率").when(col("final_risk_level") == "中风险", "定期随访").otherwise("常规护理"))
return {"age_risk_distribution": maternal_age_risk.groupBy("age_risk_level").count().collect(), "pregnancy_risk_analysis": multiple_pregnancy_risk.groupBy("pregnancy_risk").count().collect(), "medical_history_scores": medical_history_risk.select(avg("history_risk_score"), max("history_risk_score")).collect(), "gestational_complications": gestational_complications.groupBy("gestational_risk").count().collect(), "composite_scores": composite_risk_score.select(avg("total_risk_score"), max("total_risk_score"), stddev("total_risk_score")).collect(), "risk_classification": high_risk_classification.groupBy("final_risk_level").count().collect(), "gestational_risk_trends": risk_factor_analysis.collect(), "urgent_cases": urgent_intervention_cases.count(), "risk_monitoring": risk_trend_monitoring.filter(col("risk_change").isNotNull()).collect(), "care_recommendations": specialized_care_recommendations.groupBy("care_recommendation").count().collect()}
基于大数据的孕产妇健康风险数据分析系统文档展示
💖💖作者：计算机毕业设计江挽 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
