💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
基于大数据的眼科疾病数据分析系统介绍
眼科疾病数据分析系统是一个基于大数据技术栈构建的医疗数据分析平台，采用Hadoop+Spark作为核心大数据处理框架，结合Python+Django后端开发框架和MySQL数据库，前端使用Vue+ElementUI+Echarts实现数据可视化展示。系统主要包含眼科疾病数据管理、眼科临床特征分析、患者人口画像分析、患者预后生存分析、眼科风险因素分析、患者治疗模式分析等核心功能模块，通过Spark SQL进行大规模数据查询和分析，利用Pandas、NumPy等Python科学计算库进行数据处理和统计分析，最终通过数据大屏和可视化图表展示分析结果，为眼科医疗决策提供数据支撑，同时为计算机专业学生提供了一个集大数据处理、Web开发、数据可视化于一体的综合性毕业设计项目。
基于大数据的眼科疾病数据分析系统演示视频
基于大数据的眼科疾病数据分析系统演示图片
基于大数据的眼科疾病数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, when, desc
import pandas as pd
from django.http import JsonResponse
import mysql.connector
def eyecare_disease_data_management():
spark = SparkSession.builder.appName("EyecareDataManagement").config("spark.sql.adaptive.enabled", "true").getOrCreate()
eyecare_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/eyecare_db").option("dbtable", "eyecare_disease_data").option("user", "root").option("password", "password").load()
eyecare_df.createOrReplaceTempView("eyecare_data")
filtered_data = spark.sql("SELECT patient_id, disease_name, diagnosis_date, severity_level, treatment_status FROM eyecare_data WHERE diagnosis_date >= '2023-01-01'")
disease_stats = filtered_data.groupBy("disease_name").agg(count("patient_id").alias("patient_count"), avg("severity_level").alias("avg_severity"))
treatment_analysis = filtered_data.groupBy("treatment_status").agg(count("patient_id").alias("status_count"))
severity_distribution = filtered_data.groupBy("severity_level").agg(count("patient_id").alias("level_count"))
monthly_trends = spark.sql("SELECT DATE_FORMAT(diagnosis_date, 'yyyy-MM') as month, COUNT(*) as case_count FROM eyecare_data GROUP BY DATE_FORMAT(diagnosis_date, 'yyyy-MM') ORDER BY month")
top_diseases = disease_stats.orderBy(desc("patient_count")).limit(10)
critical_cases = filtered_data.filter(col("severity_level") >= 4).select("patient_id", "disease_name", "diagnosis_date")
result_data = {"disease_statistics": disease_stats.collect(), "treatment_analysis": treatment_analysis.collect(), "severity_distribution": severity_distribution.collect(), "monthly_trends": monthly_trends.collect(), "top_diseases": top_diseases.collect(), "critical_cases": critical_cases.collect()}
spark.stop()
return JsonResponse(result_data, safe=False)
def patient_profile_analysis():
spark = SparkSession.builder.appName("PatientProfileAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
patient_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/eyecare_db").option("dbtable", "patient_info").option("user", "root").option("password", "password").load()
patient_df.createOrReplaceTempView("patient_profile")
age_distribution = spark.sql("SELECT CASE WHEN age < 18 THEN 'Under 18' WHEN age BETWEEN 18 AND 35 THEN '18-35' WHEN age BETWEEN 36 AND 50 THEN '36-50' WHEN age BETWEEN 51 AND 65 THEN '51-65' ELSE 'Over 65' END as age_group, COUNT(*) as count FROM patient_profile GROUP BY age_group")
gender_analysis = patient_df.groupBy("gender").agg(count("patient_id").alias("patient_count"), avg("age").alias("avg_age"))
location_stats = patient_df.groupBy("city", "province").agg(count("patient_id").alias("patient_count"))
occupation_analysis = patient_df.groupBy("occupation").agg(count("patient_id").alias("occupation_count")).orderBy(desc("occupation_count"))
risk_factors = spark.sql("SELECT risk_factor, COUNT(*) as factor_count FROM patient_profile WHERE risk_factor IS NOT NULL GROUP BY risk_factor ORDER BY factor_count DESC")
family_history = patient_df.groupBy("family_history").agg(count("patient_id").alias("history_count"))
lifestyle_analysis = spark.sql("SELECT lifestyle_factor, AVG(age) as avg_age, COUNT(*) as count FROM patient_profile GROUP BY lifestyle_factor")
insurance_distribution = patient_df.groupBy("insurance_type").agg(count("patient_id").alias("insurance_count"))
education_level = patient_df.groupBy("education_level").agg(count("patient_id").alias("education_count"))
income_analysis = spark.sql("SELECT CASE WHEN monthly_income < 3000 THEN 'Low' WHEN monthly_income BETWEEN 3000 AND 8000 THEN 'Medium' ELSE 'High' END as income_level, COUNT(*) as count FROM patient_profile GROUP BY income_level")
profile_result = {"age_distribution": age_distribution.collect(), "gender_analysis": gender_analysis.collect(), "location_stats": location_stats.collect(), "occupation_analysis": occupation_analysis.collect(), "risk_factors": risk_factors.collect(), "family_history": family_history.collect(), "lifestyle_analysis": lifestyle_analysis.collect(), "insurance_distribution": insurance_distribution.collect(), "education_level": education_level.collect(), "income_analysis": income_analysis.collect()}
spark.stop()
return JsonResponse(profile_result, safe=False)
def patient_prognosis_survival_analysis():
spark = SparkSession.builder.appName("PrognosisSurvivalAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
prognosis_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/eyecare_db").option("dbtable", "patient_prognosis").option("user", "root").option("password", "password").load()
prognosis_df.createOrReplaceTempView("prognosis_data")
survival_rates = spark.sql("SELECT treatment_type, AVG(CASE WHEN survival_months >= 12 THEN 1.0 ELSE 0.0 END) as one_year_survival, AVG(CASE WHEN survival_months >= 24 THEN 1.0 ELSE 0.0 END) as two_year_survival FROM prognosis_data GROUP BY treatment_type")
recovery_analysis = prognosis_df.groupBy("recovery_status").agg(count("patient_id").alias("status_count"), avg("recovery_time_days").alias("avg_recovery_time"))
complication_stats = spark.sql("SELECT complication_type, COUNT(*) as complication_count, AVG(severity_score) as avg_severity FROM prognosis_data WHERE complication_type IS NOT NULL GROUP BY complication_type")
treatment_outcome = prognosis_df.groupBy("treatment_type", "outcome_category").agg(count("patient_id").alias("outcome_count"))
vision_improvement = spark.sql("SELECT treatment_type, AVG(vision_improvement_score) as avg_improvement, COUNT(*) as patient_count FROM prognosis_data GROUP BY treatment_type ORDER BY avg_improvement DESC")
relapse_analysis = prognosis_df.filter(col("relapse_indicator") == 1).groupBy("disease_type").agg(count("patient_id").alias("relapse_count"), avg("relapse_time_months").alias("avg_relapse_time"))
quality_of_life = spark.sql("SELECT age_group, AVG(quality_of_life_score) as avg_qol_score FROM prognosis_data GROUP BY age_group ORDER BY avg_qol_score DESC")
side_effects = prognosis_df.groupBy("side_effect_type").agg(count("patient_id").alias("side_effect_count")).orderBy(desc("side_effect_count"))
long_term_follow_up = spark.sql("SELECT follow_up_period, AVG(functional_score) as avg_functional_score, COUNT(*) as patient_count FROM prognosis_data WHERE follow_up_period IS NOT NULL GROUP BY follow_up_period")
prognostic_factors = spark.sql("SELECT prognostic_factor, AVG(survival_months) as avg_survival, COUNT(*) as factor_count FROM prognosis_data GROUP BY prognostic_factor ORDER BY avg_survival DESC")
prognosis_result = {"survival_rates": survival_rates.collect(), "recovery_analysis": recovery_analysis.collect(), "complication_stats": complication_stats.collect(), "treatment_outcome": treatment_outcome.collect(), "vision_improvement": vision_improvement.collect(), "relapse_analysis": relapse_analysis.collect(), "quality_of_life": quality_of_life.collect(), "side_effects": side_effects.collect(), "long_term_follow_up": long_term_follow_up.collect(), "prognostic_factors": prognostic_factors.collect()}
spark.stop()
return JsonResponse(prognosis_result, safe=False)
基于大数据的眼科疾病数据分析系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
