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基于大数据的儿童出生体重和妊娠期数据可视化分析系统-功能介绍
基于Vue+Echarts的妊娠期数据分析系统是一个集大数据处理与可视化分析于一体的综合性平台，专门针对儿童出生体重和妊娠期相关数据进行深度挖掘与展示。系统采用Hadoop+Spark作为大数据处理框架，能够高效处理海量的医疗健康数据，通过Spark SQL和Pandas、NumPy等数据分析工具对妊娠期数据进行多维度统计分析。前端采用Vue框架结合ElementUI构建用户界面，利用Echarts图表库实现丰富的数据可视化效果，包括婴儿出生体重分布、母亲年龄与体重关系、妊娠期长短分析、吸烟等行为习惯影响因素等多个分析维度。系统支持Python+Django和Java+SpringBoot两种后端技术方案，数据存储采用MySQL数据库，通过HDFS实现分布式文件存储。整个系统提供了从数据采集、清洗、分析到可视化展示的完整解决方案，为医疗健康数据分析提供了强有力的技术支撑，同时为相关研究人员提供了直观易用的数据分析工具。
基于大数据的儿童出生体重和妊娠期数据可视化分析系统-选题背景意义
选题背景 随着医疗信息化建设的不断深入，各大医院和妇幼保健机构积累了大量关于孕产妇和新生儿的数据资源。这些数据包含了母亲的年龄、身高体重、生活习惯以及婴儿出生体重、妊娠期长短等重要信息，蕴含着丰富的医学价值和研究意义。然而，传统的数据处理方式往往局限于简单的统计分析，难以从海量数据中挖掘出深层次的关联规律。随着大数据技术的成熟发展，Hadoop、Spark等分布式计算框架为处理大规模医疗数据提供了强大的技术支撑。同时，现代可视化技术的进步使得复杂的数据分析结果能够以更加直观、易理解的方式呈现给用户。在这种背景下，将大数据技术与可视化分析相结合，构建专门针对妊娠期和新生儿数据的分析系统，不仅能够充分发挥海量数据的价值，也为相关领域的研究和决策提供了新的技术路径。 选题意义 本系统的建设具有多方面的实际意义和价值。从技术角度来看，系统将大数据处理技术应用到医疗健康领域的具体场景中，为类似的数据分析项目提供了可参考的技术方案和实现思路。通过Spark等技术处理妊娠期数据，能够验证大数据技术在医疗数据分析中的实际效果和性能表现。从应用角度来看，系统能够帮助医护人员和研究者更好地理解影响婴儿出生体重的各种因素，包括母亲年龄、BMI指数、生活习惯等，为制定个性化的孕期指导方案提供数据支撑。可视化分析结果有助于发现传统统计方法难以察觉的数据规律和异常模式，提升数据分析的深度和广度。对于孕产妇群体而言，系统分析结果可以作为健康宣教的科学依据，帮助准妈妈们了解各种行为和生理因素对胎儿发育的潜在影响。虽然本系统主要作为毕业设计项目，但其设计思路和技术实现对于推动医疗数据分析技术的应用普及仍具有一定的参考价值。
基于大数据的儿童出生体重和妊娠期数据可视化分析系统-技术选型
大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 开发语言：Python+Java（两个版本都支持） 后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持） 前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery 详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy 数据库：MySQL
基于大数据的儿童出生体重和妊娠期数据可视化分析系统-视频展示
基于大数据的儿童出生体重和妊娠期数据可视化分析系统-视频展示
基于大数据的儿童出生体重和妊娠期数据可视化分析系统-图片展示
基于大数据的儿童出生体重和妊娠期数据可视化分析系统-代码展示
from pyspark.sql import SparkSession
import pandas as pd
import numpy as np
from pyspark.sql.functions import col, when, avg, count, desc
import matplotlib.pyplot as plt
import seaborn as sns
from django.http import JsonResponse
spark = SparkSession.builder.appName("PregnancyDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def birth_weight_distribution_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/pregnancy_db").option("dbtable", "birth_data").option("user", "root").option("password", "password").load()
weight_stats = df.select("bwt_g").describe().collect()
mean_weight = float([row for row in weight_stats if row[0] == 'mean'][0][1])
std_weight = float([row for row in weight_stats if row[0] == 'stddev'][0][1])
weight_categories = df.withColumn("weight_category", when(col("bwt_g") < 2500, "低体重儿").when(col("bwt_g") > 4000, "巨大儿").otherwise("正常体重")).groupBy("weight_category").agg(count("*").alias("count"), avg("bwt_g").alias("avg_weight")).orderBy(desc("count")).collect()
age_weight_relation = df.withColumn("age_group", when(col("age") < 25, "25岁以下").when(col("age") < 35, "25-34岁").otherwise("35岁以上")).groupBy("age_group").agg(avg("bwt_g").alias("avg_weight"), count("*").alias("total_count")).orderBy("age_group").collect()
bmi_weight_analysis = df.withColumn("bmi_category", when(col("BMI") < 18.5, "偏瘦").when(col("BMI") < 24, "正常").when(col("BMI") < 28, "超重").otherwise("肥胖")).groupBy("bmi_category").agg(avg("bwt_g").alias("avg_weight"), count("*").alias("count")).collect()
smoking_impact = df.groupBy("smoke_label").agg(avg("bwt_g").alias("avg_weight"), count("*").alias("count")).collect()
low_weight_risk = df.withColumn("is_low_weight", when(col("bwt_g") < 2500, 1).otherwise(0)).groupBy("smoke_label").agg((sum("is_low_weight") / count("*") * 100).alias("low_weight_rate")).collect()
gestation_weight_trend = df.withColumn("gestation_group", when(col("gestation") < 259, "早产").when(col("gestation") < 294, "足月").otherwise("过期")).groupBy("gestation_group").agg(avg("bwt_g").alias("avg_weight"), count("*").alias("count")).collect()
correlation_matrix = df.select("bwt_g", "gestation", "age", "height_cm", "weight_kg", "BMI").toPandas().corr()
result_data = {"weight_stats": {"mean": round(mean_weight, 2), "std": round(std_weight, 2)}, "categories": [{"category": row["weight_category"], "count": row["count"], "avg_weight": round(row["avg_weight"], 2)} for row in weight_categories], "age_groups": [{"age_group": row["age_group"], "avg_weight": round(row["avg_weight"], 2), "count": row["total_count"]} for row in age_weight_relation], "bmi_analysis": [{"bmi_category": row["bmi_category"], "avg_weight": round(row["avg_weight"], 2), "count": row["count"]} for row in bmi_weight_analysis], "smoking_effect": [{"smoking": row["smoke_label"], "avg_weight": round(row["avg_weight"], 2), "count": row["count"]} for row in smoking_impact], "low_weight_risks": [{"smoking": row["smoke_label"], "risk_rate": round(row["low_weight_rate"], 2)} for row in low_weight_risk], "gestation_trends": [{"gestation_type": row["gestation_group"], "avg_weight": round(row["avg_weight"], 2), "count": row["count"]} for row in gestation_weight_trend], "correlations": correlation_matrix.round(3).to_dict()}
return JsonResponse(result_data)
def maternal_factors_impact_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/pregnancy_db").option("dbtable", "birth_data").option("user", "root").option("password", "password").load()
age_gestation_analysis = df.withColumn("age_group", when(col("age") < 25, "青年产妇").when(col("age") < 35, "适龄产妇").otherwise("高龄产妇")).groupBy("age_group").agg(avg("gestation").alias("avg_gestation"), avg("bwt_g").alias("avg_weight"), count("*").alias("sample_size")).collect()
parity_comparison = df.groupBy("parity_label").agg(avg("bwt_g").alias("avg_birth_weight"), avg("gestation").alias("avg_gestation"), count("*").alias("total_cases")).collect()
height_weight_correlation = df.withColumn("height_group", when(col("height_cm") < 160, "偏矮").when(col("height_cm") < 170, "中等").otherwise("较高")).groupBy("height_group").agg(avg("bwt_g").alias("avg_baby_weight"), avg("BMI").alias("avg_bmi"), count("*").alias("count")).collect()
age_smoking_interaction = df.withColumn("age_group", when(col("age") < 25, "青年").when(col("age") < 35, "中年").otherwise("高龄")).groupBy("age_group", "smoke_label").agg(avg("bwt_g").alias("avg_weight"), count("*").alias("case_count")).orderBy("age_group", "smoke_label").collect()
bmi_gestation_effect = df.withColumn("bmi_category", when(col("BMI") < 18.5, "偏瘦").when(col("BMI") < 24, "正常").when(col("BMI") < 28, "超重").otherwise("肥胖")).groupBy("bmi_category").agg(avg("gestation").alias("avg_gestation_days"), (sum(when(col("gestation") < 259, 1).otherwise(0)) / count("*") * 100).alias("preterm_rate")).collect()
weight_category_distribution = df.withColumn("age_group", when(col("age") < 25, "25岁以下").when(col("age") < 35, "25-34岁").otherwise("35岁以上")).withColumn("weight_category", when(col("bwt_g") < 2500, "低体重").when(col("bwt_g") > 4000, "巨大儿").otherwise("正常")).groupBy("age_group", "weight_category").agg(count("*").alias("count")).collect()
maternal_weight_impact = df.withColumn("weight_group", when(col("weight_kg") < 55, "体重较轻").when(col("weight_kg") < 70, "体重适中").otherwise("体重较重")).groupBy("weight_group").agg(avg("bwt_g").alias("avg_baby_weight"), (sum(when(col("bwt_g") > 4000, 1).otherwise(0)) / count("*") * 100).alias("macrosomia_rate")).collect()
comprehensive_risk_analysis = df.withColumn("risk_score", when((col("age") > 35) & (col("smoke_label") == "吸烟") & (col("BMI") > 28), 3).when(((col("age") > 35) & (col("smoke_label") == "吸烟")) | ((col("age") > 35) & (col("BMI") > 28)) | ((col("smoke_label") == "吸烟") & (col("BMI") > 28)), 2).when((col("age") > 35) | (col("smoke_label") == "吸烟") | (col("BMI") > 28), 1).otherwise(0)).groupBy("risk_score").agg(avg("bwt_g").alias("avg_weight"), avg("gestation").alias("avg_gestation"), count("*").alias("case_num")).collect()
response_data = {"age_gestation": [{"age_group": row["age_group"], "avg_gestation": round(row["avg_gestation"], 1), "avg_weight": round(row["avg_weight"], 2), "sample_size": row["sample_size"]} for row in age_gestation_analysis], "parity_effect": [{"parity": row["parity_label"], "avg_birth_weight": round(row["avg_birth_weight"], 2), "avg_gestation": round(row["avg_gestation"], 1), "cases": row["total_cases"]} for row in parity_comparison], "height_correlation": [{"height_group": row["height_group"], "avg_baby_weight": round(row["avg_baby_weight"], 2), "avg_bmi": round(row["avg_bmi"], 1), "count": row["count"]} for row in height_weight_correlation], "age_smoking": [{"age_group": row["age_group"], "smoking": row["smoke_label"], "avg_weight": round(row["avg_weight"], 2), "cases": row["case_count"]} for row in age_smoking_interaction], "bmi_gestation": [{"bmi_category": row["bmi_category"], "avg_gestation": round(row["avg_gestation_days"], 1), "preterm_rate": round(row["preterm_rate"], 2)} for row in bmi_gestation_effect], "weight_distribution": [{"age_group": row["age_group"], "weight_category": row["weight_category"], "count": row["count"]} for row in weight_category_distribution], "maternal_weight": [{"weight_group": row["weight_group"], "avg_baby_weight": round(row["avg_baby_weight"], 2), "macrosomia_rate": round(row["macrosomia_rate"], 2)} for row in maternal_weight_impact], "risk_analysis": [{"risk_level": row["risk_score"], "avg_weight": round(row["avg_weight"], 2), "avg_gestation": round(row["avg_gestation"], 1), "cases": row["case_num"]} for row in comprehensive_risk_analysis]}
return JsonResponse(response_data)
def multi_dimensional_visualization_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/pregnancy_db").option("dbtable", "birth_data").option("user", "root").option("password", "password").load()
pandas_df = df.select("bwt_g", "gestation", "age", "height_cm", "weight_kg", "BMI", "smoke_label", "parity_label").toPandas()
correlation_heatmap_data = pandas_df.select_dtypes(include=[np.number]).corr()
scatter_data_age_weight = pandas_df.groupby(pd.cut(pandas_df['age'], bins=[0, 25, 30, 35, 45], labels=['<25', '25-29', '30-34', '≥35']))['bwt_g'].agg(['mean', 'std', 'count']).reset_index()
gestation_weight_scatter = pandas_df.groupby(pd.cut(pandas_df['gestation'], bins=range(200, 320, 10)))['bwt_g'].agg(['mean', 'median', 'count']).reset_index()
bmi_smoking_heatmap = pandas_df.groupby([pd.cut(pandas_df['BMI'], bins=[0, 18.5, 24, 28, 40], labels=['偏瘦', '正常', '超重', '肥胖']), 'smoke_label'])['bwt_g'].agg(['mean', 'count']).unstack(fill_value=0)
height_weight_bubble = pandas_df.groupby([pd.cut(pandas_df['height_cm'], bins=[140, 155, 165, 175, 185], labels=['<155', '155-164', '165-174', '≥175']), pd.cut(pandas_df['weight_kg'], bins=[40, 55, 70, 85, 120], labels=['<55', '55-69', '70-84', '≥85'])])['bwt_g'].agg(['mean', 'count']).reset_index()
risk_factor_radar = pandas_df.groupby(['smoke_label', 'parity_label']).agg({'bwt_g': ['mean', 'std'], 'gestation': ['mean', 'std'], 'age': 'mean', 'BMI': 'mean'}).round(2)
time_trend_analysis = df.withColumn("gestation_week", (col("gestation") / 7).cast("int")).groupBy("gestation_week").agg(avg("bwt_g").alias("avg_weight"), count("*").alias("sample_count")).filter(col("gestation_week").between(28, 42)).orderBy("gestation_week").collect()
abnormal_case_analysis = df.withColumn("is_abnormal", when((col("bwt_g") < 2500) | (col("bwt_g") > 4000) | (col("gestation") < 259) | (col("gestation") > 294), 1).otherwise(0)).groupBy("smoke_label", "parity_label").agg((sum("is_abnormal") / count("*") * 100).alias("abnormal_rate"), count("*").alias("total_cases")).collect()
factor_importance_score = pandas_df.corr()['bwt_g'].abs().sort_values(ascending=False).to_dict()
visualization_data = {"correlation_matrix": correlation_heatmap_data.round(3).to_dict(), "age_weight_trends": [{"age_group": row['age'], "mean_weight": round(row['mean'], 2), "std_weight": round(row['std'], 2), "sample_size": row['count']} for _, row in scatter_data_age_weight.iterrows()], "gestation_weight_curve": [{"gestation_range": str(row['gestation']), "mean_weight": round(row['mean'], 2), "median_weight": round(row['median'], 2), "count": row['count']} for _, row in gestation_weight_scatter.iterrows() if not pd.isna(row['mean'])], "bmi_smoking_matrix": bmi_smoking_heatmap.round(2).to_dict(), "height_weight_distribution": [{"height_group": row['height_cm'], "weight_group": row['weight_kg'], "avg_birth_weight": round(row['mean'], 2), "sample_count": row['count']} for _, row in height_weight_bubble.iterrows()], "risk_factor_profiles": risk_factor_radar.round(2).to_dict(), "weekly_growth_trend": [{"week": row["gestation_week"], "avg_weight": round(row["avg_weight"], 2), "sample_count": row["sample_count"]} for row in time_trend_analysis], "abnormal_rates": [{"smoking": row["smoke_label"], "parity": row["parity_label"], "abnormal_rate": round(row["abnormal_rate"], 2), "total_cases": row["total_cases"]} for row in abnormal_case_analysis], "factor_importance": {k: round(v, 3) for k, v in factor_importance_score.items()}}
return JsonResponse(visualization_data)
基于大数据的儿童出生体重和妊娠期数据可视化分析系统-结语
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