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结核病数据可视化分析系统- 简介
基于Spark+Django的结核病数据可视化分析系统是一套集大数据处理、智能分析与交互式可视化于一体的医疗数据分析平台。该系统采用Hadoop+Spark作为大数据处理引擎，能够高效处理海量结核病患者的临床数据，通过Spark SQL实现复杂的多维度关联分析。后端基于Django框架构建RESTful API接口，结合Pandas和NumPy进行数据预处理和统计计算，前端采用Vue+ElementUI+Echarts技术栈打造响应式可视化界面。系统核心功能涵盖患者基本特征与结核病关联性分析、临床核心症状与患病概率分析、生活习惯及病史风险评估，以及基于机器学习的多维关联分析。通过年龄性别分布统计、症状严重程度量化分析、特征重要性排序等算法模型，系统能够深度挖掘结核病患病规律，生成热力图、柱状图、散点图等多种可视化图表，为医疗研究人员提供直观的数据洞察和决策支持，实现了传统医疗数据分析向智能化大数据分析的技术跃升。
结核病数据可视化分析系统-技术 框架
开发语言：Python或Java（两个版本都支持）
大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
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
结核病数据可视化分析系统- 背景
结核病作为全球重要的公共卫生问题，其诊断和预防一直是医疗领域关注的焦点。传统的结核病数据分析主要依赖人工统计和简单的数据库查询，面对日益增长的患者数据量和复杂的多维度分析需求，传统方法已显露出处理效率低下、分析深度不足的局限性。随着大数据技术的快速发展，Spark等分布式计算框架为医疗数据的深度挖掘提供了新的技术路径。医疗机构积累的大量结核病患者临床数据，包含年龄、性别、症状严重程度、生活习惯等多维信息，这些数据蕴含着丰富的患病规律和关联模式，但缺乏有效的技术手段进行系统性分析。同时，医疗数据可视化技术的应用需求日益迫切，如何将复杂的统计分析结果以直观、易懂的方式呈现给医务人员，成为提高诊断效率和决策质量的关键环节。
本系统的设计与实现具有一定的理论价值和实际应用意义。从技术层面来看，该系统探索了大数据技术在医疗数据分析领域的应用模式，验证了Spark+Django技术栈处理医疗数据的可行性，为类似的医疗信息化项目提供了技术参考。通过机器学习算法挖掘结核病患病因素的关联规律，有助于加深对疾病发生机制的理解。从实用角度而言，系统能够帮助医务人员快速识别结核病高危人群，通过可视化图表直观展示患病风险分布，为临床诊断提供数据支撑。虽然这只是一个毕业设计项目，但其数据分析思路和可视化展示方法对医疗机构的信息化建设具有一定的借鉴价值。同时，该系统的实现过程也体现了计算机技术与医疗健康领域的交叉融合，展现了跨学科应用的技术潜力，为后续相关研究奠定了基础。金融数据分析与可视化系统-视频展示
结核病数据可视化分析系统-视频展示
结核病数据可视化分析系统-图片展示
结核病数据可视化分析系统-代码展示
from django.http import JsonResponse
from django.views import View
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
import json
spark = SparkSession.builder.appName("TuberculosisAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
class PatientDemographicAnalysisView(View):
def post(self, request):
data = json.loads(request.body)
df_spark = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tuberculosis_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "patient_data").option("user", "root").option("password", "password").load()
age_gender_analysis = df_spark.groupBy("Age", "Gender", "Class").count().toPandas()
age_bins = [0, 18, 35, 50, 65, 100]
age_labels = ['少年', '青年', '中年', '中老年', '老年']
age_gender_analysis['AgeGroup'] = pd.cut(age_gender_analysis['Age'], bins=age_bins, labels=age_labels, right=False)
risk_by_group = age_gender_analysis.groupby(['AgeGroup', 'Gender']).agg({'count': 'sum', 'Class': lambda x: (x == 'Positive').sum()}).reset_index()
risk_by_group['risk_rate'] = risk_by_group['Class'] / risk_by_group['count'] * 100
weight_loss_spark = df_spark.select("Weight_Loss", "Class").filter(df_spark.Weight_Loss.isNotNull())
positive_weight_loss = weight_loss_spark.filter(weight_loss_spark.Class == "Positive").agg({"Weight_Loss": "avg"}).collect()[0][0]
negative_weight_loss = weight_loss_spark.filter(weight_loss_spark.Class == "Negative").agg({"Weight_Loss": "avg"}).collect()[0][0]
weight_comparison = {"positive_avg": round(positive_weight_loss, 2), "negative_avg": round(negative_weight_loss, 2), "difference": round(positive_weight_loss - negative_weight_loss, 2)}
result = {"age_gender_risk": risk_by_group.to_dict('records'), "weight_loss_analysis": weight_comparison}
return JsonResponse(result)
class SymptomSeverityAnalysisView(View):
def post(self, request):
data = json.loads(request.body)
df_spark = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tuberculosis_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "patient_data").option("user", "root").option("password", "password").load()
cough_analysis = df_spark.groupBy("Cough_Severity").agg({"*": "count", "Class": "sum"}).withColumnRenamed("count(1)", "total_count").withColumnRenamed("sum(Class)", "positive_count").toPandas()
cough_analysis['Class'] = cough_analysis['Class'].apply(lambda x: 1 if x == 'Positive' else 0)
cough_analysis['risk_rate'] = (cough_analysis['positive_count'] / cough_analysis['total_count'] * 100).round(2)
breathlessness_analysis = df_spark.groupBy("Breathlessness").agg({"*": "count"}).withColumnRenamed("count(1)", "total_count").toPandas()
breathlessness_positive = df_spark.filter(df_spark.Class == "Positive").groupBy("Breathlessness").count().withColumnRenamed("count", "positive_count").toPandas()
breathlessness_merged = pd.merge(breathlessness_analysis, breathlessness_positive, on="Breathlessness", how="left").fillna(0)
breathlessness_merged['risk_rate'] = (breathlessness_merged['positive_count'] / breathlessness_merged['total_count'] * 100).round(2)
fatigue_fever_spark = df_spark.select("Fatigue", "Fever", "Class")
fatigue_severity = fatigue_fever_spark.groupBy("Fatigue").agg({"*": "count"}).withColumnRenamed("count(1)", "total").toPandas()
fatigue_positive = fatigue_fever_spark.filter(fatigue_fever_spark.Class == "Positive").groupBy("Fatigue").count().withColumnRenamed("count", "positive").toPandas()
fatigue_result = pd.merge(fatigue_severity, fatigue_positive, on="Fatigue", how="left").fillna(0)
fatigue_result['fatigue_risk_rate'] = (fatigue_result['positive'] / fatigue_result['total'] * 100).round(2)
key_symptoms = df_spark.select("Chest_Pain", "Night_Sweats", "Blood_in_Sputum", "Class")
chest_pain_risk = key_symptoms.filter(key_symptoms.Chest_Pain == 1).agg({"*": "count"}).collect()[0][0]
chest_pain_positive = key_symptoms.filter((key_symptoms.Chest_Pain == 1) & (key_symptoms.Class == "Positive")).count()
night_sweats_risk = key_symptoms.filter(key_symptoms.Night_Sweats == 1).agg({"*": "count"}).collect()[0][0]
night_sweats_positive = key_symptoms.filter((key_symptoms.Night_Sweats == 1) & (key_symptoms.Class == "Positive")).count()
blood_sputum_risk = key_symptoms.filter(key_symptoms.Blood_in_Sputum == 1).agg({"*": "count"}).collect()[0][0]
blood_sputum_positive = key_symptoms.filter((key_symptoms.Blood_in_Sputum == 1) & (key_symptoms.Class == "Positive")).count()
key_symptoms_result = {"chest_pain_risk": round(chest_pain_positive / chest_pain_risk * 100, 2), "night_sweats_risk": round(night_sweats_positive / night_sweats_risk * 100, 2), "blood_sputum_risk": round(blood_sputum_positive / blood_sputum_risk * 100, 2)}
result = {"cough_severity": cough_analysis.to_dict('records'), "breathlessness_severity": breathlessness_merged.to_dict('records'), "fatigue_analysis": fatigue_result.to_dict('records'), "key_symptoms": key_symptoms_result}
return JsonResponse(result)
class FeatureImportanceAnalysisView(View):
def post(self, request):
data = json.loads(request.body)
df_spark = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/tuberculosis_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "patient_data").option("user", "root").option("password", "password").load()
df_pandas = df_spark.toPandas()
le = LabelEncoder()
categorical_columns = ['Gender', 'Smoking_History', 'Previous_TB_History']
for col in categorical_columns:
df_pandas[col] = le.fit_transform(df_pandas[col].astype(str))
df_pandas['Class'] = le.fit_transform(df_pandas['Class'])
feature_columns = ['Age', 'Gender', 'Weight_Loss', 'Cough_Severity', 'Breathlessness', 'Fatigue', 'Fever', 'Chest_Pain', 'Night_Sweats', 'Blood_in_Sputum', 'Smoking_History', 'Previous_TB_History']
X = df_pandas[feature_columns]
y = df_pandas['Class']
rf_model = RandomForestClassifier(n_estimators=100, random_state=42, max_depth=10)
rf_model.fit(X, y)
feature_importance = pd.DataFrame({'feature': feature_columns, 'importance': rf_model.feature_importances_}).sort_values('importance', ascending=False)
correlation_matrix = df_pandas[feature_columns + ['Class']].corr()
correlation_with_target = correlation_matrix['Class'].drop('Class').abs().sort_values(ascending=False)
confirmed_patients = df_spark.filter(df_spark.Class == "Positive")
symptom_averages = confirmed_patients.agg({"Cough_Severity": "avg", "Breathlessness": "avg", "Fatigue": "avg"}).collect()[0]
non_confirmed_patients = df_spark.filter(df_spark.Class == "Negative")
non_symptom_averages = non_confirmed_patients.agg({"Cough_Severity": "avg", "Breathlessness": "avg", "Fatigue": "avg"}).collect()[0]
symptom_comparison = {"confirmed_cough_avg": round(symptom_averages[0], 2), "confirmed_breathlessness_avg": round(symptom_averages[1], 2), "confirmed_fatigue_avg": round(symptom_averages[2], 2), "non_confirmed_cough_avg": round(non_symptom_averages[0], 2), "non_confirmed_breathlessness_avg": round(non_symptom_averages[1], 2), "non_confirmed_fatigue_avg": round(non_symptom_averages[2], 2)}
lifestyle_risk_spark = df_spark.groupBy("Smoking_History", "Previous_TB_History").agg({"*": "count"}).withColumnRenamed("count(1)", "total_count")
lifestyle_positive = df_spark.filter(df_spark.Class == "Positive").groupBy("Smoking_History", "Previous_TB_History").count().withColumnRenamed("count", "positive_count")
lifestyle_merged = lifestyle_risk_spark.join(lifestyle_positive, ["Smoking_History", "Previous_TB_History"], "left").fillna(0).toPandas()
lifestyle_merged['compound_risk_rate'] = (lifestyle_merged['positive_count'] / lifestyle_merged['total_count'] * 100).round(2)
result = {"feature_importance": feature_importance.to_dict('records'), "correlation_analysis": correlation_with_target.to_dict(), "symptom_comparison": symptom_comparison, "lifestyle_compound_risk": lifestyle_merged.to_dict('records')}
return JsonResponse(result)
结核病数据可视化分析系统-结语
当下最热门的大数据毕设：Django+Echarts结核病数据可视化分析系统实战
GitHub高星标项目：基于Spark+Django的结核病数据可视化分析系统完整源码
为什么导师都推荐这个结核病大数据可视化分析系统？Spark+Django技术揭秘
支持我记得一键三连+关注，感谢支持，有技术问题、求源码，欢迎在评论区交流！
⚡⚡获取源码主页--> space.bilibili.com/35463818075…
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求，你也可以在主页上咨询我~~
