大數據畢業設計選題推薦-基于大數據的宮頸癌風險因素分析與可視化系統-Spark-Hadoop-Bigdata

?作者主頁：IT研究室?
個人簡介：曾從事計算機專業培訓教學，擅長Java、Python、微信小程序、Golang、安卓Android等項目實戰。接項目定制開發、代碼講解、答辯教學、文檔編寫、降重等。
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文章目錄

一、前言
二、開發環境
三、系統界面展示
四、代碼參考
五、系統視頻
結語

一、前言

系統介紹
基于大數據的宮頸癌風險因素分析與可視化系統是一個集數據存儲、分析和可視化于一體的綜合性醫療數據分析平臺。該系統采用Hadoop生態圈作為分布式存儲和計算的基礎架構，通過HDFS分布式文件系統管理大規模醫療數據，利用Spark和Spark SQL進行高效的數據處理和分析。系統后端采用Django框架構建RESTful API服務，前端基于Vue框架開發響應式用戶界面，集成ElementUI組件庫提供優質的用戶體驗，通過Echarts圖表庫實現豐富的數據可視化效果。系統支持多維度的宮頸癌風險因素分析，包括患者年齡分布、性行為史、STDs感染情況、避孕方式、吸煙史等關鍵指標的統計分析，能夠進行聚類分析識別高危人群，提供Hinselmann、Schiller、Citology等多種篩查方法的驗證對比分析。同時系統構建了綜合性的數據可視化大屏，通過多種圖表形式直觀展示風險因素分布規律，為醫療機構的臨床決策和預防工作提供數據支撐。

選題背景
宮頸癌作為女性常見的惡性腫瘤之一，在全球范圍內對女性健康構成重大威脅。當前醫療機構在宮頸癌預防和篩查過程中積累了大量的患者數據，包括人口學特征、生活習慣、性行為史、病毒感染情況等多維度信息，但這些數據往往分散存儲，缺乏有效的整合分析。傳統的數據處理方式已難以滿足海量醫療數據的分析需求，醫生和研究人員難以從復雜的數據中快速識別出關鍵的風險因素和規律。與此同時，現有的宮頸癌風險評估主要依賴于醫生的臨床經驗和單一檢查結果，缺乏基于大數據的綜合性分析工具。隨著大數據技術和機器學習算法的發展成熟，為醫療數據的深度挖掘和智能分析提供了新的技術手段。醫療機構迫切需要一個能夠處理大規模數據、進行多維度分析、提供直觀可視化展示的系統平臺，來輔助醫生進行更精準的風險評估和預防決策。

選題意義
本研究的實際意義主要體現在為醫療機構提供一個相對完整的數據分析工具，雖然作為畢業設計項目在功能深度上存在一定局限，但在數據整合和可視化方面能夠起到一定的輔助作用。系統通過整合患者的多維度信息，可以幫助醫生更全面地了解風險因素的分布規律，在一定程度上提升風險評估的效率。對于醫院信息化建設而言，該系統提供了一個可參考的技術實現方案，展示了大數據技術在醫療數據分析中的應用潛力。從技術層面來看，本項目探索了Hadoop和Spark等大數據技術在醫療領域的具體應用，為類似的醫療數據分析項目提供了一定的技術參考。同時，系統設計的可視化界面能夠讓醫療工作者更直觀地觀察數據趨勢，在日常的數據查看和初步分析中發揮一些作用。雖然本系統在算法復雜度和數據規模處理能力上還有提升空間，但作為一個初步的技術實踐，它驗證了將傳統醫療數據與現代大數據技術結合的可行性，為后續更深入的研究和開發奠定了基礎。

二、開發環境

大數據框架：Hadoop+Spark（本次沒用Hive，支持定制）
開發語言：Python+Java（兩個版本都支持）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（兩個版本都支持）
前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
詳細技術點：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
數據庫：MySQL

三、系統界面展示

基于大數據的宮頸癌風險因素分析與可視化系統界面展示：

四、代碼參考

項目實戰代碼參考：

from pyspark.sql import SparkSession
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler
from pyspark.sql.functions import col, count, avg, when, desc, asc
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import jsonspark = SparkSession.builder.appName("CervicalCancerRiskAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()class PatientRiskProfileAnalysis(View):def post(self, request):data = json.loads(request.body)df = spark.read.csv("hdfs://localhost:9000/cervical_cancer_data/risk_factors_cervical_cancer.csv", header=True, inferSchema=True)df_cleaned = df.filter(col("Age").isNotNull() & col("Number of sexual partners").isNotNull() & col("Num of pregnancies").isNotNull())feature_cols = ["Age", "Number of sexual partners", "First sexual intercourse", "Num of pregnancies", "Smokes", "STDs"]assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")df_features = assembler.transform(df_cleaned.fillna(0))kmeans = KMeans(k=4, seed=42, featuresCol="features", predictionCol="risk_cluster")model = kmeans.fit(df_features)clustered_df = model.transform(df_features)cluster_stats = clustered_df.groupBy("risk_cluster").agg(count("*").alias("patient_count"),avg("Age").alias("avg_age"),avg("Number of sexual partners").alias("avg_partners"),avg("Biopsy").alias("cancer_rate")).orderBy("cancer_rate", ascending=False)age_distribution = clustered_df.groupBy("risk_cluster").agg(count(when(col("Age") < 25, 1)).alias("age_under_25"),count(when((col("Age") >= 25) & (col("Age") < 35), 1)).alias("age_25_35"),count(when((col("Age") >= 35) & (col("Age") < 45), 1)).alias("age_35_45"),count(when(col("Age") >= 45, 1)).alias("age_over_45"))risk_indicators = clustered_df.groupBy("risk_cluster").agg(avg("STDs").alias("avg_stds_rate"),avg("Smokes").alias("avg_smoking_rate"),avg("Hormonal Contraceptives").alias("avg_contraceptive_use"))cluster_summary = cluster_stats.join(age_distribution, "risk_cluster").join(risk_indicators, "risk_cluster")result_data = cluster_summary.collect()response_data = []for row in result_data:cluster_info = {"cluster_id": row["risk_cluster"],"patient_count": row["patient_count"],"cancer_rate": round(row["cancer_rate"] * 100, 2),"avg_age": round(row["avg_age"], 1),"avg_partners": round(row["avg_partners"], 1),"age_distribution": {"under_25": row["age_under_25"],"25_35": row["age_25_35"],"35_45": row["age_35_45"],"over_45": row["age_over_45"]},"risk_indicators": {"stds_rate": round(row["avg_stds_rate"] * 100, 2),"smoking_rate": round(row["avg_smoking_rate"] * 100, 2),"contraceptive_rate": round(row["avg_contraceptive_use"] * 100, 2)}}response_data.append(cluster_info)return JsonResponse({"clusters": response_data, "total_patients": df_cleaned.count()})class ScreeningMethodValidation(View):def post(self, request):data = json.loads(request.body)df = spark.read.csv("hdfs://localhost:9000/cervical_cancer_data/risk_factors_cervical_cancer.csv", header=True, inferSchema=True)df_screening = df.select("Hinselmann", "Schiller", "Citology", "Biopsy").filter(col("Hinselmann").isNotNull() & col("Schiller").isNotNull() & col("Citology").isNotNull() & col("Biopsy").isNotNull())methods = ["Hinselmann", "Schiller", "Citology"]validation_results = {}for method in methods:tp = df_screening.filter((col(method) == 1) & (col("Biopsy") == 1)).count()tn = df_screening.filter((col(method) == 0) & (col("Biopsy") == 0)).count()fp = df_screening.filter((col(method) == 1) & (col("Biopsy") == 0)).count()fn = df_screening.filter((col(method) == 0) & (col("Biopsy") == 1)).count()total = tp + tn + fp + fnaccuracy = (tp + tn) / total if total > 0 else 0sensitivity = tp / (tp + fn) if (tp + fn) > 0 else 0specificity = tn / (tn + fp) if (tn + fp) > 0 else 0precision = tp / (tp + fp) if (tp + fp) > 0 else 0f1_score = 2 * (precision * sensitivity) / (precision + sensitivity) if (precision + sensitivity) > 0 else 0validation_results[method] = {"accuracy": round(accuracy * 100, 2),"sensitivity": round(sensitivity * 100, 2),"specificity": round(specificity * 100, 2),"precision": round(precision * 100, 2),"f1_score": round(f1_score, 3),"confusion_matrix": {"true_positive": tp,"true_negative": tn,"false_positive": fp,"false_negative": fn}}combined_positive = df_screening.filter((col("Hinselmann") == 1) | (col("Schiller") == 1) | (col("Citology") == 1))combined_analysis = combined_positive.groupBy("Biopsy").count().collect()positive_combinations = df_screening.withColumn("positive_methods_count",col("Hinselmann") + col("Schiller") + col("Citology")).groupBy("positive_methods_count", "Biopsy").count().collect()combination_analysis = {}for row in positive_combinations:method_count = row["positive_methods_count"]if method_count not in combination_analysis:combination_analysis[method_count] = {"positive_biopsy": 0, "negative_biopsy": 0}if row["Biopsy"] == 1:combination_analysis[method_count]["positive_biopsy"] = row["count"]else:combination_analysis[method_count]["negative_biopsy"] = row["count"]return JsonResponse({"individual_methods": validation_results,"combination_analysis": combination_analysis,"total_samples": df_screening.count()})class DemographicLifestyleAnalysis(View):def post(self, request):data = json.loads(request.body)df = spark.read.csv("hdfs://localhost:9000/cervical_cancer_data/risk_factors_cervical_cancer.csv", header=True, inferSchema=True)df_clean = df.filter(col("Age").isNotNull() & col("Biopsy").isNotNull())age_groups = df_clean.withColumn("age_group",when(col("Age") < 20, "Under 20").when((col("Age") >= 20) & (col("Age") < 30), "20-29").when((col("Age") >= 30) & (col("Age") < 40), "30-39").when((col("Age") >= 40) & (col("Age") < 50), "40-49").otherwise("50+"))age_analysis = age_groups.groupBy("age_group").agg(count("*").alias("total_patients"),count(when(col("Biopsy") == 1, 1)).alias("positive_cases"),avg("Biopsy").alias("cancer_rate")).orderBy("age_group")pregnancy_analysis = df_clean.filter(col("Num of pregnancies").isNotNull()).withColumn("pregnancy_group",when(col("Num of pregnancies") == 0, "0").when((col("Num of pregnancies") >= 1) & (col("Num of pregnancies") <= 2), "1-2").when((col("Num of pregnancies") >= 3) & (col("Num of pregnancies") <= 5), "3-5").otherwise("6+")).groupBy("pregnancy_group").agg(count("*").alias("total_patients"),avg("Biopsy").alias("cancer_rate")).orderBy("pregnancy_group")smoking_analysis = df_clean.filter(col("Smokes").isNotNull()).groupBy("Smokes").agg(count("*").alias("total_patients"),avg("Biopsy").alias("cancer_rate")).collect()smoking_detailed = df_clean.filter((col("Smokes") == 1) & col("Smokes (years)").isNotNull()).withColumn("smoking_years_group",when(col("Smokes (years)") < 5, "Less than 5 years").when((col("Smokes (years)") >= 5) & (col("Smokes (years)") < 15), "5-15 years").when((col("Smokes (years)") >= 15) & (col("Smokes (years)") < 25), "15-25 years").otherwise("25+ years")).groupBy("smoking_years_group").agg(count("*").alias("total_patients"),avg("Biopsy").alias("cancer_rate"),avg("Smokes (packs/year)").alias("avg_packs_per_year")).collect()contraceptive_analysis = df_clean.filter(col("Hormonal Contraceptives").isNotNull()).groupBy("Hormonal Contraceptives").agg(count("*").alias("total_patients"),avg("Biopsy").alias("cancer_rate")).collect()iud_analysis = df_clean.filter(col("IUD").isNotNull()).groupBy("IUD").agg(count("*").alias("total_patients"),avg("Biopsy").alias("cancer_rate")).collect()age_data = [{"age_group": row["age_group"], "total_patients": row["total_patients"], "positive_cases": row["positive_cases"], "cancer_rate": round(row["cancer_rate"] * 100, 2)} for row in age_analysis.collect()]pregnancy_data = [{"pregnancy_group": row["pregnancy_group"], "total_patients": row["total_patients"], "cancer_rate": round(row["cancer_rate"] * 100, 2)} for row in pregnancy_analysis.collect()]smoking_data = [{"smoking_status": "Smoker" if row["Smokes"] == 1 else "Non-smoker", "total_patients": row["total_patients"], "cancer_rate": round(row["cancer_rate"] * 100, 2)} for row in smoking_analysis]smoking_years_data = [{"years_group": row["smoking_years_group"], "total_patients": row["total_patients"], "cancer_rate": round(row["cancer_rate"] * 100, 2), "avg_packs_per_year": round(row["avg_packs_per_year"], 2)} for row in smoking_detailed]contraceptive_data = [{"method": "Hormonal" if row["Hormonal Contraceptives"] == 1 else "None", "total_patients": row["total_patients"], "cancer_rate": round(row["cancer_rate"] * 100, 2)} for row in contraceptive_analysis]iud_data = [{"method": "IUD" if row["IUD"] == 1 else "No IUD", "total_patients": row["total_patients"], "cancer_rate": round(row["cancer_rate"] * 100, 2)} for row in iud_analysis]return JsonResponse({"age_analysis": age_data,"pregnancy_analysis": pregnancy_data,"smoking_analysis": smoking_data,"smoking_years_analysis": smoking_years_data,"contraceptive_analysis": contraceptive_data,"iud_analysis": iud_data,"total_patients": df_clean.count()})