大數據畢業設計選題推薦-基于大數據的結核病數據可視化分析系統-Hadoop-Spark-數據可視化-BigData

?作者主頁：IT畢設夢工廠?
個人簡介：曾從事計算機專業培訓教學，擅長Java、Python、PHP、.NET、Node.js、GO、微信小程序、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。
?文末獲取源碼?
精彩專欄推薦???
Java項目
Python項目
安卓項目
微信小程序項目

文章目錄

一、前言
二、開發環境
三、系統界面展示
四、部分代碼設計
五、系統視頻
結語

一、前言

系統介紹
基于大數據的結核病數據可視化分析系統是一個專門針對結核病診斷與分析的智能醫療數據平臺。該系統采用Hadoop+Spark大數據架構作為底層數據處理引擎，通過Django框架構建穩定的后端服務體系，結合Vue+ElementUI+Echarts技術棧打造直觀的前端展示界面。系統能夠處理大規模結核病患者臨床數據，包括患者基本特征信息、典型臨床癥狀表現、生活習慣風險因素等多維度醫療數據。通過Spark SQL進行高效的數據查詢與統計分析，運用Pandas和NumPy進行深度數據挖掘，系統可以自動識別不同年齡段、性別群體的患病風險模式，分析咳嗽嚴重程度、呼吸困難、疲勞等核心癥狀與結核病診斷的關聯性。同時，系統還能夠評估吸煙史、既往病史等生活方式因素對疾病發生的影響程度。通過機器學習算法計算特征重要性排序，為臨床醫生提供數據驅動的診斷參考依據。整個系統將復雜的醫療數據轉化為清晰的可視化圖表，幫助醫療機構更好地理解結核病的發病規律和診斷要點。

選題背景
結核病作為全球重要的傳染性疾病之一，其診斷和治療一直是公共衛生領域的重點關注問題。傳統的結核病診斷主要依賴醫生的臨床經驗和基礎檢查手段，在面對大量患者數據時往往缺乏系統性的分析工具。隨著醫療信息化程度不斷提升，各大醫療機構積累了海量的患者臨床數據，這些數據包含了豐富的疾病特征信息和診斷規律。現有的醫療數據處理方式多以人工統計為主，處理效率低下且容易出現分析偏差。醫生在面對復雜的多癥狀組合時，難以快速準確地評估患病概率。大數據技術的快速發展為醫療數據分析提供了新的技術路徑，通過Hadoop和Spark等分布式計算框架，能夠高效處理大規模醫療數據集。數據可視化技術也為醫療決策提供了更加直觀的展示方式，使復雜的統計分析結果能夠以圖表形式清晰呈現。

選題意義
本課題的研究意義主要體現在為臨床診斷提供數據支撐和決策輔助。通過對結核病患者多維度特征的深度挖掘，能夠幫助醫生更準確地識別高危人群和典型癥狀模式。系統建立的特征重要性分析模型可以為醫療機構制定更有針對性的篩查策略提供參考。對于醫學教育而言，系統生成的可視化分析結果能夠作為教學案例，幫助醫學生更好地理解疾病的統計學特征。從技術發展角度來看，該系統探索了大數據技術在醫療健康領域的實際應用，為類似的醫療數據分析項目提供了技術實現思路。雖然作為畢業設計項目，系統的規模和復雜度相對有限，但其體現的數據驅動醫療決策理念具有一定的示范價值。系統還能夠為醫療機構的信息化建設提供參考，推動傳統醫療向智慧醫療的轉型發展。

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

基于大數據的結核病數據可視化分析系統界面展示：

四、部分代碼設計

項目實戰-代碼參考：

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, when, desc, asc
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.evaluation import BinaryClassificationEvaluator
import pandas as pd
import numpy as npspark = SparkSession.builder.appName("TuberculosisAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()def analyze_age_gender_risk():df = spark.read.csv("/data/tuberculosis_data.csv", header=True, inferSchema=True)age_groups = df.withColumn("age_group", when(col("Age") < 18, "少年").when((col("Age") >= 18) & (col("Age") < 40), "青年").when((col("Age") >= 40) & (col("Age") < 60), "中年").otherwise("老年"))risk_analysis = age_groups.groupBy("age_group", "Gender").agg(count("*").alias("total_count"),count(when(col("Class") == "Tuberculosis", True)).alias("tb_count")).withColumn("infection_rate", col("tb_count") / col("total_count"))cross_analysis = age_groups.groupBy("age_group", "Gender", "Class").count()pivot_result = cross_analysis.groupBy("age_group", "Gender").pivot("Class").sum("count").fillna(0)final_result = pivot_result.withColumn("total_patients", col("Normal") + col("Tuberculosis"))final_result = final_result.withColumn("tb_rate", col("Tuberculosis") / col("total_patients"))weight_analysis = df.groupBy("Class").agg(avg("Weight_Loss").alias("avg_weight_loss"))gender_weight = df.groupBy("Gender", "Class").agg(avg("Weight_Loss").alias("avg_weight_loss"))age_weight = age_groups.groupBy("age_group", "Class").agg(avg("Weight_Loss").alias("avg_weight_loss"))result_dict = {"age_gender_risk": final_result.orderBy("age_group", "Gender").collect(),"weight_analysis": weight_analysis.collect(),"gender_weight": gender_weight.collect(),"age_weight": age_weight.collect()}return result_dictdef analyze_clinical_symptoms():df = spark.read.csv("/data/tuberculosis_data.csv", header=True, inferSchema=True)cough_analysis = df.groupBy("Cough_Severity", "Class").count()cough_rates = cough_analysis.groupBy("Cough_Severity").pivot("Class").sum("count").fillna(0)cough_rates = cough_rates.withColumn("total", col("Normal") + col("Tuberculosis"))cough_rates = cough_rates.withColumn("tb_rate", col("Tuberculosis") / col("total"))breathlessness_analysis = df.groupBy("Breathlessness", "Class").count()breath_rates = breathlessness_analysis.groupBy("Breathlessness").pivot("Class").sum("count").fillna(0)breath_rates = breath_rates.withColumn("total", col("Normal") + col("Tuberculosis"))breath_rates = breath_rates.withColumn("tb_rate", col("Tuberculosis") / col("total"))fatigue_analysis = df.groupBy("Fatigue", "Class").count()fatigue_rates = fatigue_analysis.groupBy("Fatigue").pivot("Class").sum("count").fillna(0)fatigue_rates = fatigue_rates.withColumn("total", col("Normal") + col("Tuberculosis"))fatigue_rates = fatigue_rates.withColumn("tb_rate", col("Tuberculosis") / col("total"))fever_analysis = df.groupBy("Fever", "Class").count()fever_rates = fever_analysis.groupBy("Fever").pivot("Class").sum("count").fillna(0)fever_rates = fever_rates.withColumn("total", col("Normal") + col("Tuberculosis"))fever_rates = fever_rates.withColumn("tb_rate", col("Tuberculosis") / col("total"))key_symptoms = df.select("Chest_Pain", "Night_Sweats", "Blood_in_Sputum", "Class")chest_pain_stats = key_symptoms.groupBy("Chest_Pain", "Class").count()night_sweats_stats = key_symptoms.groupBy("Night_Sweats", "Class").count()blood_sputum_stats = key_symptoms.groupBy("Blood_in_Sputum", "Class").count()symptom_correlation = df.groupBy("Class").agg(avg("Cough_Severity").alias("avg_cough"),avg("Breathlessness").alias("avg_breathlessness"),avg("Fatigue").alias("avg_fatigue"))return {"cough_analysis": cough_rates.orderBy("Cough_Severity").collect(),"breath_analysis": breath_rates.orderBy("Breathlessness").collect(),"fatigue_analysis": fatigue_rates.orderBy("Fatigue").collect(),"fever_analysis": fever_rates.collect(),"chest_pain": chest_pain_stats.collect(),"night_sweats": night_sweats_stats.collect(),"blood_sputum": blood_sputum_stats.collect(),"symptom_avg": symptom_correlation.collect()}def feature_importance_analysis():df = spark.read.csv("/data/tuberculosis_data.csv", header=True, inferSchema=True)encoded_df = df.withColumn("Gender_encoded", when(col("Gender") == "Male", 1).otherwise(0))encoded_df = encoded_df.withColumn("Chest_Pain_encoded", when(col("Chest_Pain") == "Yes", 1).otherwise(0))encoded_df = encoded_df.withColumn("Night_Sweats_encoded", when(col("Night_Sweats") == "Yes", 1).otherwise(0))encoded_df = encoded_df.withColumn("Blood_in_Sputum_encoded", when(col("Blood_in_Sputum") == "Yes", 1).otherwise(0))encoded_df = encoded_df.withColumn("Smoking_encoded", when(col("Smoking_History") == "Never", 0).when(col("Smoking_History") == "Former", 1).otherwise(2))encoded_df = encoded_df.withColumn("TB_History_encoded", when(col("Previous_TB_History") == "Yes", 1).otherwise(0))encoded_df = encoded_df.withColumn("Class_encoded", when(col("Class") == "Tuberculosis", 1).otherwise(0))feature_cols = ["Age", "Gender_encoded", "Cough_Severity", "Breathlessness", "Fatigue", "Weight_Loss", "Chest_Pain_encoded", "Night_Sweats_encoded", "Blood_in_Sputum_encoded", "Smoking_encoded", "TB_History_encoded"]assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")feature_df = assembler.transform(encoded_df)train_data, test_data = feature_df.randomSplit([0.8, 0.2], seed=42)rf = RandomForestClassifier(featuresCol="features", labelCol="Class_encoded", numTrees=100, seed=42)rf_model = rf.fit(train_data)feature_importance = rf_model.featureImportances.toArray()importance_dict = dict(zip(feature_cols, feature_importance))sorted_importance = sorted(importance_dict.items(), key=lambda x: x[1], reverse=True)predictions = rf_model.transform(test_data)evaluator = BinaryClassificationEvaluator(labelCol="Class_encoded", rawPredictionCol="rawPrediction")auc = evaluator.evaluate(predictions)correlation_matrix = encoded_df.select(feature_cols + ["Class_encoded"]).toPandas().corr()tb_patients = encoded_df.filter(col("Class_encoded") == 1)normal_patients = encoded_df.filter(col("Class_encoded") == 0)tb_stats = tb_patients.agg(*[avg(col(c)).alias(f"{c}_tb_avg") for c in feature_cols]).collect()[0]normal_stats = normal_patients.agg(*[avg(col(c)).alias(f"{c}_normal_avg") for c in feature_cols]).collect()[0]return {"feature_importance": sorted_importance,"model_auc": auc,"correlation_matrix": correlation_matrix.to_dict(),"tb_patient_stats": tb_stats.asDict(),"normal_patient_stats": normal_stats.asDict()}