💖💖作者：计算机毕业设计江挽 💙💙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
基于大数据的青光眼数据分析系统介绍
青光眼数据分析系统是一款基于大数据技术栈的医疗数据分析平台，采用Python作为主要开发语言，整合Hadoop分布式存储和Spark大数据处理框架构建核心数据处理引擎。系统前端采用Vue+ElementUI+Echarts技术栈打造现代化的用户交互界面，后端基于Django框架提供稳定的API服务支撑，数据存储层使用MySQL数据库管理患者信息和临床数据。系统核心功能涵盖青光眼信息管理、数据可视化大屏、患者群体画像分析、核心临床指标分析、疾病风险因素分析、症状用药分析以及青光眼亚型分析等多个专业模块。通过Spark SQL进行大规模医疗数据的快速查询和分析，结合Pandas和NumPy进行深度数据挖掘，系统能够从海量青光眼患者数据中提取有价值的医疗洞察。数据可视化大屏运用Echarts图表库将复杂的医疗统计数据转化为直观的图表展示，为医疗工作者提供全面的青光眼疾病分析视角，支持多维度的患者画像构建和风险评估分析。
基于大数据的青光眼数据分析系统演示视频
基于大数据的青光眼数据分析系统演示图片
基于大数据的青光眼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, asc
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, FloatType, DateType
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("GlaucomaAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def patient_profile_analysis(request):
patient_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/glaucoma_db").option("dbtable", "patient_info").option("user", "root").option("password", "password").load()
age_distribution = patient_data.groupBy("age_group").agg(count("patient_id").alias("count"), avg("eye_pressure").alias("avg_pressure")).orderBy("age_group")
gender_stats = patient_data.groupBy("gender").agg(count("patient_id").alias("total_patients"), avg("visual_field_defect").alias("avg_defect_rate")).collect()
risk_level_analysis = patient_data.groupBy("risk_level").agg(count("patient_id").alias("patient_count"), avg("treatment_duration").alias("avg_duration")).orderBy(desc("patient_count"))
regional_distribution = patient_data.groupBy("region").agg(count("patient_id").alias("region_count"), avg("diagnosis_delay_days").alias("avg_delay")).orderBy(desc("region_count"))
comorbidity_analysis = patient_data.filter(col("has_diabetes") == 1).groupBy("hypertension_status").agg(count("patient_id").alias("diabetic_patients")).collect()
age_risk_correlation = patient_data.groupBy("age_group", "risk_level").agg(count("patient_id").alias("count")).orderBy("age_group", "risk_level")
treatment_response = patient_data.filter(col("treatment_months") > 6).groupBy("initial_pressure_level").agg(avg("pressure_reduction_percentage").alias("avg_reduction")).collect()
family_history_impact = patient_data.groupBy("family_history").agg(count("patient_id").alias("total"), avg("onset_age").alias("avg_onset_age")).collect()
severity_distribution = patient_data.groupBy("disease_severity").agg(count("patient_id").alias("severity_count"), avg("cup_disc_ratio").alias("avg_cdr")).orderBy("disease_severity")
medication_adherence = patient_data.filter(col("on_medication") == 1).groupBy("adherence_level").agg(count("patient_id").alias("patient_count"), avg("pressure_control_rate").alias("control_rate")).collect()
profile_results = {"age_distribution": [row.asDict() for row in age_distribution.collect()], "gender_stats": [row.asDict() for row in gender_stats], "risk_analysis": [row.asDict() for row in risk_level_analysis.collect()], "regional_data": [row.asDict() for row in regional_distribution.collect()], "severity_data": [row.asDict() for row in severity_distribution.collect()]}
return JsonResponse({"status": "success", "data": profile_results, "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")})
def clinical_indicators_analysis(request):
clinical_data = spark.read.format("jdbc").option("url", "jdbc://localhost:3306/glaucoma_db").option("dbtable", "clinical_indicators").option("user", "root").option("password", "password").load()
pressure_trend_analysis = clinical_data.groupBy("measurement_month").agg(avg("intraocular_pressure").alias("avg_pressure"), count("patient_id").alias("measurement_count")).orderBy("measurement_month")
visual_field_progression = clinical_data.filter(col("visual_field_test_date").isNotNull()).groupBy("test_period").agg(avg("mean_deviation").alias("avg_md"), avg("pattern_standard_deviation").alias("avg_psd")).collect()
optic_nerve_analysis = clinical_data.groupBy("nerve_fiber_thickness_category").agg(count("patient_id").alias("category_count"), avg("cup_disc_ratio").alias("avg_cdr"), avg("rim_area").alias("avg_rim")).collect()
pressure_control_effectiveness = clinical_data.filter(col("treatment_start_date").isNotNull()).groupBy("pressure_control_status").agg(count("patient_id").alias("control_count"), avg("pressure_reduction").alias("avg_reduction")).collect()
progression_risk_indicators = clinical_data.groupBy("progression_risk_score").agg(count("patient_id").alias("risk_count"), avg("visual_field_loss_rate").alias("avg_loss_rate")).orderBy(desc("progression_risk_score"))
seasonal_pressure_variation = clinical_data.groupBy("measurement_season").agg(avg("intraocular_pressure").alias("seasonal_avg_pressure"), count("measurement_id").alias("seasonal_measurements")).collect()
medication_response_analysis = clinical_data.filter(col("medication_type").isNotNull()).groupBy("medication_category").agg(avg("pressure_drop_percentage").alias("avg_pressure_drop"), count("patient_id").alias("medication_users")).collect()
diagnostic_accuracy_metrics = clinical_data.groupBy("diagnostic_method").agg(count("diagnosis_id").alias("method_usage"), avg("diagnostic_confidence_score").alias("avg_confidence")).orderBy(desc("avg_confidence"))
follow_up_compliance = clinical_data.groupBy("follow_up_interval_weeks").agg(count("patient_id").alias("interval_patients"), avg("pressure_stability_score").alias("stability_score")).collect()
correlation_matrix_data = clinical_data.select("intraocular_pressure", "visual_field_defect", "cup_disc_ratio", "nerve_fiber_thickness").toPandas()
correlation_results = np.corrcoef([correlation_matrix_data["intraocular_pressure"], correlation_matrix_data["visual_field_defect"], correlation_matrix_data["cup_disc_ratio"], correlation_matrix_data["nerve_fiber_thickness"]])
clinical_results = {"pressure_trends": [row.asDict() for row in pressure_trend_analysis.collect()], "visual_field_data": [row.asDict() for row in visual_field_progression], "optic_nerve_data": [row.asDict() for row in optic_nerve_analysis], "progression_indicators": [row.asDict() for row in progression_risk_indicators.collect()], "correlation_matrix": correlation_results.tolist()}
return JsonResponse({"status": "success", "data": clinical_results, "analysis_timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")})
def risk_factors_analysis(request):
risk_data = spark.read.format("jdbc").option("url", "jdbc://localhost:3306/glaucoma_db").option("dbtable", "risk_factors").option("user", "root").option("password", "password").load()
demographic_risk_analysis = risk_data.groupBy("age_category", "gender").agg(count("patient_id").alias("demographic_count"), avg("overall_risk_score").alias("avg_risk_score")).orderBy("age_category", "gender")
lifestyle_risk_factors = risk_data.groupBy("smoking_status").agg(count("patient_id").alias("smoker_count"), avg("disease_progression_rate").alias("progression_rate"), avg("treatment_response_score").alias("response_score")).collect()
genetic_predisposition = risk_data.filter(col("family_history_glaucoma") == 1).groupBy("genetic_risk_level").agg(count("patient_id").alias("genetic_patients"), avg("onset_age").alias("avg_onset")).collect()
comorbidity_risk_assessment = risk_data.groupBy("diabetes_status", "hypertension_status").agg(count("patient_id").alias("comorbid_count"), avg("compound_risk_score").alias("compound_risk")).collect()
environmental_factors = risk_data.groupBy("occupational_exposure").agg(count("patient_id").alias("exposure_count"), avg("environmental_risk_score").alias("env_risk_score")).orderBy(desc("env_risk_score"))
medication_risk_interactions = risk_data.filter(col("concurrent_medications").isNotNull()).groupBy("medication_interaction_level").agg(count("patient_id").alias("interaction_patients"), avg("adverse_event_rate").alias("adverse_rate")).collect()
behavioral_risk_patterns = risk_data.groupBy("exercise_frequency", "stress_level").agg(count("patient_id").alias("behavior_count"), avg("lifestyle_risk_contribution").alias("lifestyle_risk")).collect()
temporal_risk_evolution = risk_data.groupBy("risk_assessment_year").agg(avg("baseline_risk_score").alias("yearly_avg_risk"), count("patient_id").alias("assessed_patients")).orderBy("risk_assessment_year")
predictive_risk_modeling = risk_data.select("age", "baseline_pressure", "family_history_score", "lifestyle_factors_score", "final_risk_category").toPandas()
risk_feature_importance = predictive_risk_modeling.groupby("final_risk_category").agg({"age": "mean", "baseline_pressure": "mean", "family_history_score": "mean", "lifestyle_factors_score": "mean"}).reset_index()
intervention_effectiveness = risk_data.filter(col("received_intervention") == 1).groupBy("intervention_type").agg(count("patient_id").alias("intervention_count"), avg("risk_reduction_percentage").alias("risk_reduction")).collect()
longitudinal_risk_tracking = risk_data.groupBy("follow_up_duration_months").agg(avg("risk_score_change").alias("avg_risk_change"), count("patient_id").alias("tracked_patients")).orderBy("follow_up_duration_months")
risk_results = {"demographic_risks": [row.asDict() for row in demographic_risk_analysis.collect()], "lifestyle_factors": [row.asDict() for row in lifestyle_risk_factors], "genetic_data": [row.asDict() for row in genetic_predisposition], "environmental_risks": [row.asDict() for row in environmental_factors.collect()], "temporal_evolution": [row.asDict() for row in temporal_risk_evolution.collect()], "feature_importance": risk_feature_importance.to_dict("records")}
return JsonResponse({"status": "success", "data": risk_results, "model_timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")})
基于大数据的青光眼数据分析系统文档展示
💖💖作者：计算机毕业设计江挽 💙💙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
