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基于大数据的宫颈癌风险因素分析与可视化系统-功能介绍
基于大数据的宫颈癌风险因素分析与可视化系统是一个集数据处理、分析挖掘与可视化展示于一体的医疗健康大数据应用系统。该系统采用Hadoop分布式存储架构结合Spark大数据计算引擎，对宫颈癌患者的多维度风险因素数据进行深度挖掘和智能分析。系统通过Python和Java双语言支持，后端采用Django和SpringBoot框架提供稳定的数据服务接口，前端运用Vue+ElementUI构建现代化的用户交互界面，结合Echarts图表库实现数据的多样化可视化呈现。系统核心功能涵盖患者基础信息管理、多维度风险因素关联分析、聚类算法驱动的患者画像构建以及交互式数据可视化展示。通过Spark SQL进行大规模数据查询优化，利用Pandas和NumPy进行精确的数据预处理和统计计算，系统能够从年龄分布、生育史、吸烟行为、性行为特征、性传播疾病史等多个维度深入探索宫颈癌发病的潜在规律，为医疗健康决策提供数据支撑。
基于大数据的宫颈癌风险因素分析与可视化系统-选题背景意义
选题背景 宫颈癌作为全球女性面临的重要健康威胁，其发病机制复杂且涉及多重风险因素的交互作用。随着医疗信息化进程的不断推进，医院和健康机构积累了海量的患者诊疗数据，这些数据蕴含着丰富的疾病发病规律和风险预测信息。传统的医疗数据分析主要依赖统计学方法和小样本研究，面对大规模、多维度的医疗数据时显得力不从心，难以充分挖掘数据中隐藏的深层关联模式。宫颈癌的风险因素包括年龄、生育史、性行为特征、病毒感染史等多个复杂维度，这些因素之间存在着错综复杂的相互影响关系，需要借助现代大数据技术才能进行全面深入的分析。医疗大数据分析技术的兴起为解决这一难题提供了新的思路和工具，通过分布式计算和机器学习算法，能够处理大规模医疗数据集并发现传统方法难以识别的数据模式。 选题意义 本课题的研究具有多重现实意义和应用价值。从技术角度来看，通过构建基于Hadoop+Spark的大数据分析平台，能够验证分布式计算技术在医疗健康领域的实际应用效果，为相关技术在医疗行业的推广应用提供参考案例。从医疗实践角度分析，系统通过对多维度风险因素的量化分析和可视化展示，有助于医护人员更直观地理解不同因素对宫颈癌发病的影响程度，为制定个性化的预防和筛查策略提供数据依据。患者风险画像的构建能够帮助识别高危人群，为精准医疗和预防性干预提供科学支撑。可视化分析结果也便于医疗教育和健康宣传工作的开展，通过直观的图表展示让公众更好地理解疾病风险因素。从学术研究层面来说，系统整合了数据挖掘、机器学习和可视化技术，为医疗大数据分析提供了一个相对完整的技术解决方案。虽然作为毕业设计项目，其研究深度和应用规模相对有限，但仍能为后续更深入的医疗大数据研究奠定基础，同时也体现了跨学科融合在解决实际问题中的重要作用。
基于大数据的宫颈癌风险因素分析与可视化系统-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
基于大数据的宫颈癌风险因素分析与可视化系统-视频展示
基于大数据的宫颈癌风险因素分析与可视化系统-图片展示
基于大数据的宫颈癌风险因素分析与可视化系统-代码展示
from pyspark.sql import SparkSession
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler
from pyspark.sql.functions import col, count, when, isnan, isnull, mean, stddev
import pandas as pd
import numpy as np
from django.http import JsonResponse
import json
spark = SparkSession.builder.appName("CervicalCancerAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def multi_dimensional_risk_analysis(request):
df = spark.read.csv("hdfs://localhost:9000/cervical_cancer/risk_factors_cervical_cancer.csv", header=True, inferSchema=True)
cleaned_df = df.na.fill(0)
age_risk_df = cleaned_df.groupBy("Age").agg(
count("*").alias("total_count"),
count(when(col("Biopsy") == 1, True)).alias("positive_count")
)
age_risk_df = age_risk_df.withColumn("risk_rate", col("positive_count") / col("total_count"))
age_pandas_df = age_risk_df.toPandas()
age_bins = pd.cut(age_pandas_df['Age'], bins=[0, 25, 35, 45, 55, 100], labels=['<25', '25-34', '35-44', '45-54', '55+'])
age_pandas_df['age_group'] = age_bins
age_group_analysis = age_pandas_df.groupby('age_group').agg({
'total_count': 'sum',
'positive_count': 'sum'
}).reset_index()
age_group_analysis['group_risk_rate'] = age_group_analysis['positive_count'] / age_group_analysis['total_count']
pregnancy_risk_df = cleaned_df.groupBy("Num of pregnancies").agg(
count("*").alias("total_count"),
count(when(col("Biopsy") == 1, True)).alias("positive_count")
)
pregnancy_risk_df = pregnancy_risk_df.withColumn("risk_rate", col("positive_count") / col("total_count"))
pregnancy_pandas_df = pregnancy_risk_df.toPandas()
smoking_analysis_df = cleaned_df.filter(col("Smokes") == 1)
smoking_correlation = smoking_analysis_df.stat.corr("Smokes (years)", "Biopsy")
smoking_packs_correlation = smoking_analysis_df.stat.corr("Smokes (packs/year)", "Biopsy")
stds_risk_df = cleaned_df.groupBy("STDs").agg(
count("*").alias("total_count"),
count(when(col("Biopsy") == 1, True)).alias("positive_count")
)
stds_risk_df = stds_risk_df.withColumn("risk_rate", col("positive_count") / col("total_count"))
hpv_analysis_df = cleaned_df.groupBy("STDs:HPV").agg(
count("*").alias("total_count"),
count(when(col("Biopsy") == 1, True)).alias("positive_count")
)
hpv_analysis_df = hpv_analysis_df.withColumn("hpv_risk_rate", col("positive_count") / col("total_count"))
sexual_partners_analysis = cleaned_df.groupBy("Number of sexual partners").agg(
count("*").alias("total_count"),
count(when(col("Biopsy") == 1, True)).alias("positive_count")
).withColumn("partners_risk_rate", col("positive_count") / col("total_count"))
result_data = {
'age_group_analysis': age_group_analysis.to_dict('records'),
'pregnancy_analysis': pregnancy_pandas_df.to_dict('records'),
'smoking_years_correlation': float(smoking_correlation),
'smoking_packs_correlation': float(smoking_packs_correlation),
'stds_analysis': stds_risk_df.toPandas().to_dict('records'),
'hpv_analysis': hpv_analysis_df.toPandas().to_dict('records'),
'sexual_partners_analysis': sexual_partners_analysis.toPandas().to_dict('records')
}
return JsonResponse(result_data, safe=False)
def patient_clustering_analysis(request):
df = spark.read.csv("hdfs://localhost:9000/cervical_cancer/risk_factors_cervical_cancer.csv", header=True, inferSchema=True)
cleaned_df = df.na.fill(0)
feature_cols = ["Age", "Number of sexual partners", "First sexual intercourse", "Num of pregnancies", "Smokes", "STDs"]
assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
feature_df = assembler.transform(cleaned_df)
kmeans = KMeans(k=4, seed=42, featuresCol="features", predictionCol="cluster")
model = kmeans.fit(feature_df)
clustered_df = model.transform(feature_df)
cluster_stats = clustered_df.groupBy("cluster").agg(
count("*").alias("cluster_size"),
mean("Age").alias("avg_age"),
mean("Number of sexual partners").alias("avg_sexual_partners"),
mean("First sexual intercourse").alias("avg_first_intercourse"),
mean("Num of pregnancies").alias("avg_pregnancies"),
mean("Smokes").alias("smoking_rate"),
mean("STDs").alias("stds_rate"),
count(when(col("Biopsy") == 1, True)).alias("positive_cases")
)
cluster_stats = cluster_stats.withColumn("cluster_risk_rate", col("positive_cases") / col("cluster_size"))
cluster_pandas_df = cluster_stats.toPandas()
cluster_pandas_df['risk_level'] = pd.cut(cluster_pandas_df['cluster_risk_rate'],
bins=[0, 0.1, 0.3, 1.0],
labels=['低风险', '中风险', '高风险'])
detailed_cluster_analysis = []
for cluster_id in range(4):
cluster_data = clustered_df.filter(col("cluster") == cluster_id)
cluster_features = cluster_data.select(feature_cols + ["Biopsy"]).toPandas()
feature_correlations = {}
for feature in feature_cols:
correlation = cluster_features[feature].corr(cluster_features['Biopsy'])
feature_correlations[feature] = float(correlation) if not pd.isna(correlation) else 0.0
detailed_cluster_analysis.append({
'cluster_id': int(cluster_id),
'feature_correlations': feature_correlations,
'cluster_characteristics': cluster_pandas_df[cluster_pandas_df['cluster'] == cluster_id].iloc[0].to_dict()
})
high_risk_features = clustered_df.filter(col("cluster_risk_rate") > 0.3).select(feature_cols).describe().toPandas()
result_data = {
'cluster_summary': cluster_pandas_df.to_dict('records'),
'detailed_analysis': detailed_cluster_analysis,
'high_risk_profile': high_risk_features.to_dict('records'),
'total_clusters': 4
}
return JsonResponse(result_data, safe=False)
def screening_methods_effectiveness_analysis(request):
df = spark.read.csv("hdfs://localhost:9000/cervical_cancer/risk_factors_cervical_cancer.csv", header=True, inferSchema=True)
cleaned_df = df.na.fill(0)
screening_methods = ["Hinselmann", "Schiller", "Citology"]
effectiveness_results = {}
for method in screening_methods:
confusion_matrix = cleaned_df.groupBy(method, "Biopsy").count().toPandas()
confusion_pivot = confusion_matrix.pivot(index=method, columns='Biopsy', values='count').fillna(0)
if 0 in confusion_pivot.columns and 1 in confusion_pivot.columns:
true_negative = confusion_pivot.loc[0, 0] if 0 in confusion_pivot.index else 0
false_positive = confusion_pivot.loc[0, 1] if 0 in confusion_pivot.index else 0
false_negative = confusion_pivot.loc[1, 0] if 1 in confusion_pivot.index else 0
true_positive = confusion_pivot.loc[1, 1] if 1 in confusion_pivot.index else 0
total_cases = true_positive + true_negative + false_positive + false_negative
accuracy = (true_positive + true_negative) / total_cases if total_cases > 0 else 0
sensitivity = true_positive / (true_positive + false_negative) if (true_positive + false_negative) > 0 else 0
specificity = true_negative / (true_negative + false_positive) if (true_negative + false_positive) > 0 else 0
precision = true_positive / (true_positive + false_positive) if (true_positive + false_positive) > 0 else 0
f1_score = 2 * (precision * sensitivity) / (precision + sensitivity) if (precision + sensitivity) > 0 else 0
effectiveness_results[method] = {
'accuracy': float(accuracy),
'sensitivity': float(sensitivity),
'specificity': float(specificity),
'precision': float(precision),
'f1_score': float(f1_score),
'true_positive': int(true_positive),
'true_negative': int(true_negative),
'false_positive': int(false_positive),
'false_negative': int(false_negative)
}
combined_screening_df = cleaned_df.withColumn("positive_count",
col("Hinselmann") + col("Schiller") + col("Citology"))
combined_analysis = combined_screening_df.groupBy("positive_count").agg(
count("*").alias("total_cases"),
count(when(col("Biopsy") == 1, True)).alias("confirmed_cases")
)
combined_analysis = combined_analysis.withColumn("confirmation_rate",
col("confirmed_cases") / col("total_cases"))
combined_pandas_df = combined_analysis.toPandas()
best_combination_analysis = combined_pandas_df.loc[combined_pandas_df['confirmation_rate'].idxmax()] if not combined_pandas_df.empty else None
method_comparison = []
for method, metrics in effectiveness_results.items():
method_comparison.append({
'method': method,
'overall_score': (metrics['accuracy'] + metrics['sensitivity'] + metrics['specificity']) / 3,
'clinical_value': metrics['sensitivity'] * 0.6 + metrics['specificity'] * 0.4,
'metrics': metrics
})
method_comparison.sort(key=lambda x: x['overall_score'], reverse=True)
result_data = {
'individual_methods': effectiveness_results,
'combined_screening': combined_pandas_df.to_dict('records'),
'best_combination': best_combination_analysis.to_dict() if best_combination_analysis is not None else {},
'method_ranking': method_comparison,
'screening_recommendations': {
'highest_accuracy': max(effectiveness_results.items(), key=lambda x: x[1]['accuracy'])[0],
'highest_sensitivity': max(effectiveness_results.items(), key=lambda x: x[1]['sensitivity'])[0],
'best_overall': method_comparison[0]['method'] if method_comparison else 'Unknown'
}
}
return JsonResponse(result_data, safe=False)
基于大数据的宫颈癌风险因素分析与可视化系统-结语
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