💖💖作者：计算机编程小咖 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
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丙型肝炎患者数据可视化分析系统介绍
基于大数据的丙型肝炎患者数据可视化分析系统是一个集成了现代大数据处理技术与医疗数据分析的综合性应用系统，该系统采用Hadoop分布式存储框架结合Spark大数据计算引擎作为核心技术架构，能够高效处理和分析大规模的丙型肝炎患者医疗数据。系统前端采用Vue框架搭配ElementUI组件库和Echarts可视化图表库，为用户提供直观友好的交互界面和丰富的数据展示效果，后端支持Django和Spring Boot双重框架选择，配合MySQL数据库实现稳定的数据存储和管理。系统核心功能涵盖了完整的数据分析流程，包括丙型肝炎患者数据的统一管理、患者基本特征的深度分析、群体特征的对比研究、疾病进展的动态追踪，以及临床应用价值的量化评估，特别是生化指标关联性分析功能，能够通过Spark SQL和Pandas、NumPy等数据处理工具，挖掘出患者各项生化指标之间的潜在关联关系。系统还配备了大屏看板功能，通过Echarts实现数据的实时可视化展示，让医疗工作者能够一目了然地掌握患者群体的整体状况和趋势变化，同时具备完善的用户权限管理、系统公告发布、可视化图表管理等辅助功能，为医疗机构提供了一个功能全面、技术先进的丙型肝炎患者数据分析和决策支持平台。
丙型肝炎患者数据可视化分析系统演示视频
丙型肝炎患者数据可视化分析系统演示图片
丙型肝炎患者数据可视化分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("HepatitisAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def patient_basic_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hepatitis_db").option("dbtable", "patient_info").option("user", "root").option("password", "password").load()
age_distribution = df.groupBy(when(col("age") < 30, "30岁以下").when((col("age") >= 30) & (col("age") < 50), "30-50岁").when((col("age") >= 50) & (col("age") < 70), "50-70岁").otherwise("70岁以上").alias("age_group")).count().collect()
gender_distribution = df.groupBy("gender").count().collect()
region_distribution = df.groupBy("region").count().collect()
severity_stats = df.groupBy("disease_severity").agg(avg("treatment_duration").alias("avg_duration"), count("*").alias("patient_count")).collect()
monthly_trend = df.groupBy(date_format("diagnosis_date", "yyyy-MM").alias("month")).count().orderBy("month").collect()
age_severity_cross = df.groupBy("age_group", "disease_severity").count().collect()
treatment_response = df.groupBy("treatment_response").agg(avg("age").alias("avg_age"), countDistinct("patient_id").alias("patient_num")).collect()
result_data = {"age_distribution": [{"age_group": row.age_group, "count": row.count} for row in age_distribution], "gender_distribution": [{"gender": row.gender, "count": row.count} for row in gender_distribution], "region_distribution": [{"region": row.region, "count": row.count} for row in region_distribution], "severity_stats": [{"severity": row.disease_severity, "avg_duration": float(row.avg_duration), "patient_count": row.patient_count} for row in severity_stats], "monthly_trend": [{"month": row.month, "count": row.count} for row in monthly_trend], "age_severity_cross": [{"age_group": row.age_group, "severity": row.disease_severity, "count": row.count} for row in age_severity_cross], "treatment_response": [{"response": row.treatment_response, "avg_age": float(row.avg_age), "patient_num": row.patient_num} for row in treatment_response]}
return JsonResponse(result_data)
@csrf_exempt
def biochemical_correlation_analysis(request):
biochemical_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hepatitis_db").option("dbtable", "biochemical_indicators").option("user", "root").option("password", "password").load()
alt_ast_corr = biochemical_df.select("alt", "ast").filter((col("alt").isNotNull()) & (col("ast").isNotNull())).collect()
alt_values = [float(row.alt) for row in alt_ast_corr]
ast_values = [float(row.ast) for row in alt_ast_corr]
correlation_alt_ast = np.corrcoef(alt_values, ast_values)[0, 1]
bilirubin_albumin_data = biochemical_df.select("total_bilirubin", "albumin").filter((col("total_bilirubin").isNotNull()) & (col("albumin").isNotNull())).collect()
bilirubin_values = [float(row.total_bilirubin) for row in bilirubin_albumin_data]
albumin_values = [float(row.albumin) for row in bilirubin_albumin_data]
correlation_bilirubin_albumin = np.corrcoef(bilirubin_values, albumin_values)[0, 1]
platelet_wbc_data = biochemical_df.select("platelet_count", "white_blood_cell").filter((col("platelet_count").isNotNull()) & (col("white_blood_cell").isNotNull())).collect()
platelet_values = [float(row.platelet_count) for row in platelet_wbc_data]
wbc_values = [float(row.white_blood_cell) for row in platelet_wbc_data]
correlation_platelet_wbc = np.corrcoef(platelet_values, wbc_values)[0, 1]
severity_indicators = biochemical_df.join(spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hepatitis_db").option("dbtable", "patient_info").option("user", "root").option("password", "password").load(), "patient_id").groupBy("disease_severity").agg(avg("alt").alias("avg_alt"), avg("ast").alias("avg_ast"), avg("total_bilirubin").alias("avg_bilirubin"), avg("albumin").alias("avg_albumin")).collect()
abnormal_indicators = biochemical_df.filter((col("alt") > 40) | (col("ast") > 40) | (col("total_bilirubin") > 21) | (col("albumin") < 35)).groupBy("patient_id").agg(count("*").alias("abnormal_count")).collect()
indicator_ranges = biochemical_df.agg(min("alt").alias("min_alt"), max("alt").alias("max_alt"), avg("alt").alias("avg_alt"), min("ast").alias("min_ast"), max("ast").alias("max_ast"), avg("ast").alias("avg_ast"), min("total_bilirubin").alias("min_bilirubin"), max("total_bilirubin").alias("max_bilirubin"), avg("total_bilirubin").alias("avg_bilirubin")).collect()[0]
result_data = {"correlations": {"alt_ast": float(correlation_alt_ast), "bilirubin_albumin": float(correlation_bilirubin_albumin), "platelet_wbc": float(correlation_platelet_wbc)}, "severity_indicators": [{"severity": row.disease_severity, "avg_alt": float(row.avg_alt), "avg_ast": float(row.avg_ast), "avg_bilirubin": float(row.avg_bilirubin), "avg_albumin": float(row.avg_albumin)} for row in severity_indicators], "abnormal_analysis": [{"patient_id": row.patient_id, "abnormal_count": row.abnormal_count} for row in abnormal_indicators], "indicator_ranges": {"alt": {"min": float(indicator_ranges.min_alt), "max": float(indicator_ranges.max_alt), "avg": float(indicator_ranges.avg_alt)}, "ast": {"min": float(indicator_ranges.min_ast), "max": float(indicator_ranges.max_ast), "avg": float(indicator_ranges.avg_ast)}, "bilirubin": {"min": float(indicator_ranges.min_bilirubin), "max": float(indicator_ranges.max_bilirubin), "avg": float(indicator_ranges.avg_bilirubin)}}}
return JsonResponse(result_data)
@csrf_exempt
def disease_progression_analysis(request):
progression_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hepatitis_db").option("dbtable", "disease_progression").option("user", "root").option("password", "password").load()
patient_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hepatitis_db").option("dbtable", "patient_info").option("user", "root").option("password", "password").load()
combined_df = progression_df.join(patient_df, "patient_id")
stage_transition = combined_df.groupBy("previous_stage", "current_stage").count().orderBy("previous_stage", "current_stage").collect()
progression_speed = combined_df.withColumn("progression_days", datediff(col("current_check_date"), col("previous_check_date"))).groupBy("current_stage").agg(avg("progression_days").alias("avg_days"), min("progression_days").alias("min_days"), max("progression_days").alias("max_days")).collect()
age_progression = combined_df.withColumn("age_group", when(col("age") < 40, "40岁以下").when((col("age") >= 40) & (col("age") < 60), "40-60岁").otherwise("60岁以上")).groupBy("age_group", "current_stage").count().collect()
treatment_progression = combined_df.groupBy("treatment_type", "current_stage").agg(count("*").alias("patient_count"), avg("viral_load").alias("avg_viral_load")).collect()
monthly_progression = combined_df.groupBy(date_format("current_check_date", "yyyy-MM").alias("check_month"), "current_stage").count().orderBy("check_month").collect()
severity_change = combined_df.filter(col("previous_stage") != col("current_stage")).groupBy("patient_id").agg(collect_list("current_stage").alias("stage_history"), count("*").alias("change_count")).collect()
viral_load_trend = combined_df.groupBy("current_stage").agg(avg("viral_load").alias("avg_viral_load"), min("viral_load").alias("min_viral_load"), max("viral_load").alias("max_viral_load"), stddev("viral_load").alias("std_viral_load")).collect()
result_data = {"stage_transitions": [{"previous_stage": row.previous_stage, "current_stage": row.current_stage, "count": row.count} for row in stage_transition], "progression_speed": [{"stage": row.current_stage, "avg_days": float(row.avg_days), "min_days": row.min_days, "max_days": row.max_days} for row in progression_speed], "age_progression": [{"age_group": row.age_group, "stage": row.current_stage, "count": row.count} for row in age_progression], "treatment_progression": [{"treatment": row.treatment_type, "stage": row.current_stage, "patient_count": row.patient_count, "avg_viral_load": float(row.avg_viral_load)} for row in treatment_progression], "monthly_progression": [{"month": row.check_month, "stage": row.current_stage, "count": row.count} for row in monthly_progression], "severity_changes": [{"patient_id": row.patient_id, "stage_history": row.stage_history, "change_count": row.change_count} for row in severity_change], "viral_load_analysis": [{"stage": row.current_stage, "avg_viral_load": float(row.avg_viral_load), "min_viral_load": float(row.min_viral_load), "max_viral_load": float(row.max_viral_load), "std_viral_load": float(row.std_viral_load)} for row in viral_load_trend]}
return JsonResponse(result_data)
丙型肝炎患者数据可视化分析系统文档展示
💖💖作者：计算机编程小咖
💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！
💛💛想说的话：感谢大家的关注与支持！
💜💜
网站实战项目
安卓/小程序实战项目
大数据实战项目
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