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文章目录

一、前言
二、开发环境
三、系统界面展示
四、部分代码设计
五、系统视频
结语

一、前言

系统介绍
基于大数据的结核病数据可视化分析系统是一个专门针对结核病诊断与分析的智能医疗数据平台。该系统采用Hadoop+Spark大数据架构作为底层数据处理引擎，通过Django框架构建稳定的后端服务体系，结合Vue+ElementUI+Echarts技术栈打造直观的前端展示界面。系统能够处理大规模结核病患者临床数据，包括患者基本特征信息、典型临床症状表现、生活习惯风险因素等多维度医疗数据。通过Spark SQL进行高效的数据查询与统计分析，运用Pandas和NumPy进行深度数据挖掘，系统可以自动识别不同年龄段、性别群体的患病风险模式，分析咳嗽严重程度、呼吸困难、疲劳等核心症状与结核病诊断的关联性。同时，系统还能够评估吸烟史、既往病史等生活方式因素对疾病发生的影响程度。通过机器学习算法计算特征重要性排序，为临床医生提供数据驱动的诊断参考依据。整个系统将复杂的医疗数据转化为清晰的可视化图表，帮助医疗机构更好地理解结核病的发病规律和诊断要点。

选题背景
结核病作为全球重要的传染性疾病之一，其诊断和治疗一直是公共卫生领域的重点关注问题。传统的结核病诊断主要依赖医生的临床经验和基础检查手段，在面对大量患者数据时往往缺乏系统性的分析工具。随着医疗信息化程度不断提升，各大医疗机构积累了海量的患者临床数据，这些数据包含了丰富的疾病特征信息和诊断规律。现有的医疗数据处理方式多以人工统计为主，处理效率低下且容易出现分析偏差。医生在面对复杂的多症状组合时，难以快速准确地评估患病概率。大数据技术的快速发展为医疗数据分析提供了新的技术路径，通过Hadoop和Spark等分布式计算框架，能够高效处理大规模医疗数据集。数据可视化技术也为医疗决策提供了更加直观的展示方式，使复杂的统计分析结果能够以图表形式清晰呈现。

选题意义
本课题的研究意义主要体现在为临床诊断提供数据支撑和决策辅助。通过对结核病患者多维度特征的深度挖掘，能够帮助医生更准确地识别高危人群和典型症状模式。系统建立的特征重要性分析模型可以为医疗机构制定更有针对性的筛查策略提供参考。对于医学教育而言，系统生成的可视化分析结果能够作为教学案例，帮助医学生更好地理解疾病的统计学特征。从技术发展角度来看，该系统探索了大数据技术在医疗健康领域的实际应用，为类似的医疗数据分析项目提供了技术实现思路。虽然作为毕业设计项目，系统的规模和复杂度相对有限，但其体现的数据驱动医疗决策理念具有一定的示范价值。系统还能够为医疗机构的信息化建设提供参考，推动传统医疗向智慧医疗的转型发展。

二、开发环境

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

三、系统界面展示

基于大数据的结核病数据可视化分析系统界面展示：

四、部分代码设计

项目实战-代码参考：

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()}