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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
基于大数据的儿童出生体重和妊娠期数据可视化分析系统是一个采用现代大数据技术栈构建的医疗数据分析平台，专门针对儿童出生体重与妊娠期相关数据进行深度挖掘和可视化展示。系统以Hadoop分布式文件系统作为数据存储基础，结合Spark大数据计算引擎进行高效的数据处理和分析，前端采用Vue框架配合ElementUI组件库和Echarts图表库构建直观的用户交互界面，后端基于SpringBoot框架提供稳定的API服务支撑。系统能够处理大规模的医疗统计数据，通过多维度的数据分析模型，深入探索母亲年龄、BMI指数、生育情况、吸烟习惯等因素与婴儿出生体重、妊娠期长短之间的关联关系，并通过丰富的图表形式将分析结果进行可视化呈现，为医疗研究人员和相关从业者提供数据支持和决策参考。
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推进，医院和妇幼保健机构积累了大量的孕产妇和新生儿相关数据，这些数据蕴含着丰富的医学规律和健康信息。儿童出生体重作为评估新生儿健康状况的重要指标，与妊娠期长短、母亲健康状况等多种因素密切相关，传统的统计分析方法在面对海量、多维度的医疗数据时显得力不从心。当前医疗数据分析领域正朝着大数据化、智能化方向发展，运用Hadoop、Spark等大数据技术对医疗数据进行深度挖掘已成为趋势。妊娠期数据分析对于优生优育、母婴健康管理具有重要价值，但现有的分析工具多停留在简单的统计层面，缺乏深层次的关联性分析和直观的可视化展示。大数据技术的快速发展为处理复杂医疗数据提供了新的技术路径，Spark在处理结构化数据方面的优势使得对孕产妇数据的实时分析成为可能。 选题意义 本课题的研究具有一定的理论价值和实际应用价值。从技术层面来看，项目将大数据处理技术应用于医疗健康数据分析领域，探索了Hadoop分布式存储与Spark计算引擎在医疗数据处理中的实际应用效果，为类似的医疗数据分析项目提供了技术参考和实现思路。从应用角度来说，系统通过多维度的数据分析帮助发现影响儿童出生体重的潜在因素，为医护人员制定个性化的孕期指导方案提供数据支撑，同时可视化的分析结果能够帮助孕妇更直观地了解各种因素对胎儿发育的影响。项目构建的数据分析模型可以为妇幼保健机构的健康管理工作提供一定的辅助作用，通过数据挖掘识别高危孕妇群体，有助于医疗资源的合理配置。当然，作为毕业设计项目，其主要意义还在于提升学生对大数据技术的理解和实际应用能力，培养数据分析思维和系统开发技能，为今后从事相关技术工作打下基础。
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
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.springframework.stereotype.Service;
import java.util.HashMap;
import java.util.Map;
import java.util.List;
import java.util.ArrayList;
@Service
public class DataAnalysisService {
private SparkSession spark = SparkSession.builder()
.appName("ChildBirthWeightAnalysis")
.master("local[*]")
.config("spark.sql.adaptive.enabled", "true")
.config("spark.sql.adaptive.coalescePartitions.enabled", "true")
.getOrCreate();
public Map<String, Object> analyzeBirthWeightDistribution() {
Dataset<Row> dataset = spark.read()
.format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/birth_analysis")
.option("dbtable", "birth_data")
.option("user", "root")
.option("password", "password")
.load();
dataset.createOrReplaceTempView("birth_weight_data");
Dataset<Row> weightStats = spark.sql(
"SELECT " +
"CASE " +
" WHEN bwt_g < 2500 THEN '低体重儿' " +
" WHEN bwt_g >= 2500 AND bwt_g <= 4000 THEN '正常体重' " +
" ELSE '巨大儿' " +
"END as weight_category, " +
"COUNT(*) as count, " +
"ROUND(AVG(bwt_g), 2) as avg_weight, " +
"ROUND(STDDEV(bwt_g), 2) as std_weight " +
"FROM birth_weight_data " +
"WHERE bwt_g IS NOT NULL " +
"GROUP BY weight_category " +
"ORDER BY avg_weight"
);
List<Row> results = weightStats.collectAsList();
Map<String, Object> analysisResult = new HashMap<>();
List<Map<String, Object>> distributions = new ArrayList<>();
int totalCount = 0;
for (Row row : results) {
Map<String, Object> item = new HashMap<>();
item.put("category", row.getString(0));
item.put("count", row.getLong(1));
item.put("avgWeight", row.getDouble(2));
item.put("stdWeight", row.getDouble(3));
distributions.add(item);
totalCount += row.getLong(1);
}
for (Map<String, Object> item : distributions) {
long count = (Long) item.get("count");
double percentage = Math.round((double) count / totalCount * 100 * 100.0) / 100.0;
item.put("percentage", percentage);
}
analysisResult.put("distributions", distributions);
analysisResult.put("totalSamples", totalCount);
return analysisResult;
}
public Map<String, Object> analyzeMaternageFactorsCorrelation() {
Dataset<Row> dataset = spark.read()
.format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/birth_analysis")
.option("dbtable", "birth_data")
.option("user", "root")
.option("password", "password")
.load();
dataset.createOrReplaceTempView("maternal_data");
Dataset<Row> ageGroupAnalysis = spark.sql(
"SELECT " +
"CASE " +
" WHEN age < 25 THEN '年轻产妇' " +
" WHEN age >= 25 AND age < 35 THEN '适龄产妇' " +
" ELSE '高龄产妇' " +
"END as age_group, " +
"ROUND(AVG(bwt_g), 2) as avg_birth_weight, " +
"ROUND(AVG(gestation), 2) as avg_gestation, " +
"COUNT(*) as sample_count, " +
"ROUND(AVG(BMI), 2) as avg_bmi " +
"FROM maternal_data " +
"WHERE age IS NOT NULL AND bwt_g IS NOT NULL AND gestation IS NOT NULL " +
"GROUP BY age_group " +
"ORDER BY avg_birth_weight DESC"
);
Dataset<Row> smokingImpact = spark.sql(
"SELECT " +
"smoke_label, " +
"ROUND(AVG(bwt_g), 2) as avg_birth_weight, " +
"ROUND(AVG(gestation), 2) as avg_gestation, " +
"COUNT(*) as sample_count, " +
"ROUND(COUNT(CASE WHEN bwt_g < 2500 THEN 1 END) * 100.0 / COUNT(*), 2) as low_weight_rate " +
"FROM maternal_data " +
"WHERE smoke_label IS NOT NULL AND bwt_g IS NOT NULL " +
"GROUP BY smoke_label"
);
Dataset<Row> bmiCorrelation = spark.sql(
"SELECT " +
"CASE " +
" WHEN BMI < 18.5 THEN '偏瘦' " +
" WHEN BMI >= 18.5 AND BMI < 25 THEN '正常' " +
" WHEN BMI >= 25 AND BMI < 30 THEN '超重' " +
" ELSE '肥胖' " +
"END as bmi_category, " +
"ROUND(AVG(bwt_g), 2) as avg_birth_weight, " +
"ROUND(AVG(gestation), 2) as avg_gestation, " +
"COUNT(*) as sample_count " +
"FROM maternal_data " +
"WHERE BMI IS NOT NULL AND bwt_g IS NOT NULL AND gestation IS NOT NULL " +
"GROUP BY bmi_category " +
"ORDER BY avg_birth_weight"
);
Map<String, Object> correlationResult = new HashMap<>();
correlationResult.put("ageGroupAnalysis", ageGroupAnalysis.collectAsList());
correlationResult.put("smokingImpact", smokingImpact.collectAsList());
correlationResult.put("bmiCorrelation", bmiCorrelation.collectAsList());
return correlationResult;
}
public Map<String, Object> generateAdvancedStatistics() {
Dataset<Row> dataset = spark.read()
.format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/birth_analysis")
.option("dbtable", "birth_data")
.option("user", "root")
.option("password", "password")
.load();
dataset.createOrReplaceTempView("advanced_analysis");
Dataset<Row> multiFactorAnalysis = spark.sql(
"SELECT " +
"age_group, " +
"smoke_label, " +
"BMI_category, " +
"ROUND(AVG(bwt_g), 2) as avg_weight, " +
"ROUND(AVG(gestation), 2) as avg_gestation, " +
"COUNT(*) as sample_size, " +
"ROUND(STDDEV(bwt_g), 2) as weight_std, " +
"MIN(bwt_g) as min_weight, " +
"MAX(bwt_g) as max_weight " +
"FROM (" +
" SELECT *, " +
" CASE WHEN age < 25 THEN '年轻' WHEN age >= 35 THEN '高龄' ELSE '适龄' END as age_group, " +
" CASE WHEN BMI < 18.5 THEN '偏瘦' WHEN BMI >= 25 THEN '超重' ELSE '正常' END as BMI_category " +
" FROM advanced_analysis " +
" WHERE age IS NOT NULL AND BMI IS NOT NULL AND smoke_label IS NOT NULL AND bwt_g IS NOT NULL " +
") temp " +
"GROUP BY age_group, smoke_label, BMI_category " +
"HAVING COUNT(*) >= 5 " +
"ORDER BY avg_weight DESC"
);
Dataset<Row> gestationRiskAnalysis = spark.sql(
"SELECT " +
"CASE " +
" WHEN gestation < 259 THEN '早产' " +
" WHEN gestation >= 259 AND gestation <= 287 THEN '足月' " +
" ELSE '过期' " +
"END as gestation_category, " +
"smoke_label, " +
"COUNT(*) as total_count, " +
"ROUND(AVG(bwt_g), 2) as avg_birth_weight, " +
"ROUND(COUNT(CASE WHEN bwt_g < 2500 THEN 1 END) * 100.0 / COUNT(*), 2) as low_weight_risk, " +
"ROUND(COUNT(CASE WHEN bwt_g > 4000 THEN 1 END) * 100.0 / COUNT(*), 2) as high_weight_risk " +
"FROM advanced_analysis " +
"WHERE gestation IS NOT NULL AND smoke_label IS NOT NULL AND bwt_g IS NOT NULL " +
"GROUP BY gestation_category, smoke_label " +
"ORDER BY gestation_category, smoke_label"
);
Dataset<Row> correlationMatrix = spark.sql(
"SELECT " +
"ROUND(CORR(bwt_g, gestation), 4) as weight_gestation_corr, " +
"ROUND(CORR(bwt_g, age), 4) as weight_age_corr, " +
"ROUND(CORR(bwt_g, BMI), 4) as weight_bmi_corr, " +
"ROUND(CORR(gestation, age), 4) as gestation_age_corr, " +
"ROUND(CORR(gestation, BMI), 4) as gestation_bmi_corr, " +
"ROUND(CORR(age, BMI), 4) as age_bmi_corr " +
"FROM advanced_analysis " +
"WHERE bwt_g IS NOT NULL AND gestation IS NOT NULL AND age IS NOT NULL AND BMI IS NOT NULL"
);
Map<String, Object> advancedStats = new HashMap<>();
advancedStats.put("multiFactorAnalysis", multiFactorAnalysis.collectAsList());
advancedStats.put("gestationRiskAnalysis", gestationRiskAnalysis.collectAsList());
advancedStats.put("correlationMatrix", correlationMatrix.first());
return advancedStats;
}
}
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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