大數據畢業設計選題推薦-基于大數據的分化型甲狀腺癌復發數據可視化分析系統-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
基于大數據的分化型甲狀腺癌復發數據可視化分析系統是一個專門針對甲狀腺癌患者臨床數據進行深度分析的智能化平臺。該系統采用Hadoop+Spark大數據架構，結合Django后端框架和Vue前端技術，構建了完整的數據處理與可視化分析流程。系統以383例分化型甲狀腺癌患者的15年隨訪數據為基礎，涵蓋13個關鍵臨床病理特征，包括患者基本信息、病理分期、治療反應、甲狀腺功能等多維度指標。通過Spark SQL進行大規模數據處理，運用Pandas和NumPy進行統計分析，采用ECharts實現交互式數據可視化，系統能夠從患者人口統計學特征、臨床病理核心特征、治療效果指標、甲狀腺功能狀態等多個維度進行關聯分析。平臺提供了熱力圖關聯性分析、風險分層可視化、復發預測模型等核心功能，支持醫生快速識別影響甲狀腺癌復發的關鍵因素，為臨床決策提供數據支撐，同時為醫學研究人員提供了便捷的數據探索工具。

選題背景
分化型甲狀腺癌作為內分泌系統最常見的惡性腫瘤，其發病率在全球范圍內呈現持續上升趨勢，已成為嚴重威脅人類健康的重要疾病。盡管分化型甲狀腺癌整體預后相對較好，但術后復發問題依然是臨床關注的焦點，復發率可達10-30%，給患者帶來沉重的身心負擔和經濟壓力。傳統的醫療數據分析方法在處理大規模、多維度的臨床數據時存在明顯局限性，醫生往往只能基于有限的統計指標進行經驗性判斷，難以全面把握影響復發的復雜因素關系。隨著醫療信息化的深入發展和臨床數據的快速積累，如何運用先進的大數據技術挖掘隱藏在海量醫療數據中的有價值信息，識別影響甲狀腺癌復發的關鍵因素模式，已經成為現代精準醫學發展的迫切需求。

選題意義
本研究具有重要的理論價值和實踐意義，能夠為甲狀腺癌的臨床診療和醫學研究提供有力支撐。從理論角度來看，該系統通過構建多維度數據分析模型，深入挖掘患者基本特征、病理參數、治療反應等因素與復發風險之間的關聯規律，豐富了甲狀腺癌復發機制的理論認知，為建立更加科學的風險評估體系提供了數據基礎。從實踐應用層面來說，該系統能夠輔助臨床醫生快速識別高風險患者群體，制定個性化的隨訪監測方案，提高早期發現復發的能力，降低治療成本，改善患者預后質量。該平臺還為醫學研究人員提供了便捷的數據探索工具，支持大規模臨床數據的統計分析和可視化展示，有助于開展循證醫學研究，推動甲狀腺癌診療規范的不斷完善。雖然作為畢業設計項目，系統規模和復雜度相對有限，但其設計理念和技術方案為醫療大數據分析系統的開發提供了可行的參考模式。

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

基于大數據的分化型甲狀腺癌復發數據可視化分析系統界面展示：

四、代碼參考

項目實戰代碼參考：

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, when, count, avg, corr, collect_list
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.stat import Correlation
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import jsonspark = SparkSession.builder.appName("ThyroidCancerAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()class MultiFactorCorrelationAnalysis(View):def post(self, request):df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/thyroid_db").option("dbtable", "thyroid_data").option("user", "root").option("password", "password").load()numeric_columns = ['Age', 'Gender_encoded', 'Smoking_encoded', 'Hx_Smoking_encoded', 'Hx_Radiothreapy_encoded', 'Thyroid_Function_encoded', 'Physical_Examination_encoded', 'Adenopathy_encoded', 'Pathology_encoded', 'Focality_encoded', 'Risk_encoded', 'T_encoded', 'N_encoded', 'M_encoded', 'Stage_encoded', 'Response_encoded', 'Recurred_encoded']feature_assembler = VectorAssembler(inputCols=numeric_columns, outputCol="features")feature_df = feature_assembler.transform(df)correlation_matrix = Correlation.corr(feature_df, "features", "pearson").head()[0].toArray()correlation_data = []for i, col1 in enumerate(numeric_columns):for j, col2 in enumerate(numeric_columns):correlation_data.append({'x_factor': col1,'y_factor': col2,'correlation_value': float(correlation_matrix[i][j])})recurrence_correlations = []for i, column in enumerate(numeric_columns[:-1]):corr_value = correlation_matrix[i][-1]recurrence_correlations.append({'factor_name': column,'correlation_with_recurrence': float(corr_value),'correlation_strength': 'strong' if abs(corr_value) > 0.5 else 'moderate' if abs(corr_value) > 0.3 else 'weak'})high_risk_factors = df.filter(col("Risk_encoded") == 2)intermediate_risk_factors = df.filter(col("Risk_encoded") == 1)low_risk_factors = df.filter(col("Risk_encoded") == 0)risk_factor_analysis = {'high_risk_recurrence_rate': high_risk_factors.filter(col("Recurred_encoded") == 1).count() / high_risk_factors.count(),'intermediate_risk_recurrence_rate': intermediate_risk_factors.filter(col("Recurred_encoded") == 1).count() / intermediate_risk_factors.count(),'low_risk_recurrence_rate': low_risk_factors.filter(col("Recurred_encoded") == 1).count() / low_risk_factors.count()}return JsonResponse({'correlation_matrix': correlation_data,'recurrence_correlations': recurrence_correlations,'risk_factor_analysis': risk_factor_analysis,'total_patients': df.count()})class PatientDemographicAnalysis(View):def post(self, request):df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/thyroid_db").option("dbtable", "thyroid_data").option("user", "root").option("password", "password").load()age_distribution = df.withColumn("age_group", when(col("Age") < 30, "20-29").when(col("Age") < 40, "30-39").when(col("Age") < 50, "40-49").when(col("Age") < 60, "50-59").otherwise("60+")).groupBy("age_group").agg(count("*").alias("patient_count"), avg("Age").alias("avg_age")).collect()gender_recurrence_analysis = df.groupBy("Gender").agg(count("*").alias("total_patients"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_patients")).withColumn("recurrence_rate", col("recurred_patients") / col("total_patients")).collect()smoking_analysis = df.groupBy("Smoking", "Hx_Smoking").agg(count("*").alias("total_patients"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_patients")).withColumn("recurrence_rate", col("recurred_patients") / col("total_patients")).collect()radiotherapy_analysis = df.groupBy("Hx_Radiothreapy").agg(count("*").alias("total_patients"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_patients")).withColumn("recurrence_rate", col("recurred_patients") / col("total_patients")).collect()age_gender_cross_analysis = df.withColumn("age_group", when(col("Age") < 40, "Young").otherwise("Elder")).groupBy("age_group", "Gender").agg(count("*").alias("patient_count"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_count")).withColumn("recurrence_rate", col("recurred_count") / col("patient_count")).collect()lifestyle_risk_factors = df.groupBy("Smoking", "Hx_Smoking", "Hx_Radiothreapy").agg(count("*").alias("total_patients"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_patients")).withColumn("combined_risk_score", when((col("Smoking") == "Yes") | (col("Hx_Smoking") == "Yes") | (col("Hx_Radiothreapy") == "Yes"), 1).otherwise(0)).withColumn("recurrence_rate", col("recurred_patients") / col("total_patients")).collect()demographic_summary = {'total_patients': df.count(),'average_age': df.agg(avg("Age")).collect()[0][0],'gender_distribution': df.groupBy("Gender").count().collect(),'overall_recurrence_rate': df.filter(col("Recurred") == "Yes").count() / df.count()}return JsonResponse({'age_distribution': [row.asDict() for row in age_distribution],'gender_recurrence_analysis': [row.asDict() for row in gender_recurrence_analysis],'smoking_analysis': [row.asDict() for row in smoking_analysis],'radiotherapy_analysis': [row.asDict() for row in radiotherapy_analysis],'age_gender_cross_analysis': [row.asDict() for row in age_gender_cross_analysis],'lifestyle_risk_factors': [row.asDict() for row in lifestyle_risk_factors],'demographic_summary': demographic_summary})class ClinicalPathologyAnalysis(View):def post(self, request):df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/thyroid_db").option("dbtable", "thyroid_data").option("user", "root").option("password", "password").load()tnm_staging_analysis = df.groupBy("T", "N", "M", "Stage").agg(count("*").alias("total_patients"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_patients")).withColumn("recurrence_rate", col("recurred_patients") / col("total_patients")).orderBy(col("recurrence_rate").desc()).collect()pathology_type_analysis = df.groupBy("Pathology").agg(count("*").alias("total_patients"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_patients")).withColumn("recurrence_rate", col("recurred_patients") / col("total_patients")).orderBy(col("recurrence_rate").desc()).collect()risk_stratification_analysis = df.groupBy("Risk").agg(count("*").alias("total_patients"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_patients")).withColumn("recurrence_rate", col("recurred_patients") / col("total_patients")).collect()focality_analysis = df.groupBy("Focality").agg(count("*").alias("total_patients"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_patients")).withColumn("recurrence_rate", col("recurred_patients") / col("total_patients")).collect()t_stage_detailed = df.groupBy("T").agg(count("*").alias("total_patients"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_patients")).withColumn("tumor_stage_risk", when(col("T").isin(["T3a", "T3b", "T4a", "T4b"]), "High").when(col("T").isin(["T2"]), "Intermediate").otherwise("Low")).withColumn("recurrence_rate", col("recurred_patients") / col("total_patients")).collect()n_stage_detailed = df.groupBy("N").agg(count("*").alias("total_patients"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_patients")).withColumn("lymph_node_risk", when(col("N").isin(["N1a", "N1b"]), "High").otherwise("Low")).withColumn("recurrence_rate", col("recurred_patients") / col("total_patients")).collect()pathology_focality_cross = df.groupBy("Pathology", "Focality").agg(count("*").alias("total_patients"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_patients")).withColumn("recurrence_rate", col("recurred_patients") / col("total_patients")).collect()advanced_stage_analysis = df.filter(col("Stage").isin(["III", "IV", "IVA", "IVB"])).groupBy("Stage", "Pathology").agg(count("*").alias("total_patients"), count(when(col("Recurred") == "Yes", 1)).alias("recurred_patients")).withColumn("recurrence_rate", col("recurred_patients") / col("total_patients")).collect()return JsonResponse({'tnm_staging_analysis': [row.asDict() for row in tnm_staging_analysis],'pathology_type_analysis': [row.asDict() for row in pathology_type_analysis],'risk_stratification_analysis': [row.asDict() for row in risk_stratification_analysis],'focality_analysis': [row.asDict() for row in focality_analysis],'t_stage_detailed': [row.asDict() for row in t_stage_detailed],'n_stage_detailed': [row.asDict() for row in n_stage_detailed],'pathology_focality_cross': [row.asDict() for row in pathology_focality_cross],'advanced_stage_analysis': [row.asDict() for row in advanced_stage_analysis]})