💖💖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
基于大数据的眼癌数据分析与可视化系统介绍
眼癌数据分析与可视化系统是一个基于大数据技术栈构建的医疗数据处理平台，采用Hadoop分布式存储架构和Spark大数据计算引擎作为底层技术支撑，通过Django后端框架提供RESTful API接口服务，前端采用Vue.js结合ElementUI组件库和Echarts图表库实现用户交互界面。系统主要针对眼癌患者的医疗数据进行深度挖掘和可视化展示，涵盖系统首页导航、个人中心管理、用户权限控制、眼癌数据管理、患者基本画像分析、眼癌临床特征分析、治疗方案效果分析、核心生存预后分析以及系统管理等九大核心功能模块。通过整合患者的基本信息、临床检查数据、治疗记录和随访资料，运用Spark SQL进行数据清洗和特征提取，结合Pandas和NumPy进行统计分析，最终以直观的图表形式呈现分析结果，为医疗工作者提供数据支持和决策参考，同时为眼癌患者的诊疗过程提供科学的数据分析工具。
基于大数据的眼癌数据分析与可视化系统演示视频
基于大数据的眼癌数据分析与可视化系统演示图片
基于大数据的眼癌数据分析与可视化系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
from django.http import JsonResponse
from django.views import View
import pandas as pd
import numpy as np
import json
from decimal import Decimal
spark = SparkSession.builder.appName("EyeCancerAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
class PatientPortraitAnalysis(View):
def post(self, request):
data = json.loads(request.body)
patient_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/eyecancer").option("dbtable", "patient_info").option("user", "root").option("password", "123456").load()
age_distribution = patient_data.groupBy(when(col("age") < 18, "儿童").when((col("age") >= 18) & (col("age") < 60), "成年").otherwise("老年").alias("age_group")).count().collect()
gender_distribution = patient_data.groupBy("gender").count().collect()
location_stats = patient_data.groupBy("location").count().orderBy(desc("count")).collect()
tumor_type_stats = patient_data.groupBy("tumor_type").count().collect()
stage_distribution = patient_data.groupBy("tumor_stage").count().collect()
risk_factors = patient_data.groupBy("risk_factor").count().orderBy(desc("count")).collect()
family_history = patient_data.groupBy("family_history").count().collect()
occupation_stats = patient_data.groupBy("occupation").count().orderBy(desc("count")).limit(10).collect()
education_stats = patient_data.groupBy("education_level").count().collect()
income_stats = patient_data.groupBy("income_level").count().collect()
smoking_stats = patient_data.groupBy("smoking_status").count().collect()
alcohol_stats = patient_data.groupBy("alcohol_status").count().collect()
exercise_stats = patient_data.groupBy("exercise_habit").count().collect()
diet_stats = patient_data.groupBy("diet_habit").count().collect()
sleep_stats = patient_data.groupBy("sleep_quality").count().collect()
result = {"age_distribution": [{"name": row.age_group, "value": row["count"]} for row in age_distribution], "gender_distribution": [{"name": row.gender, "value": row["count"]} for row in gender_distribution], "location_stats": [{"name": row.location, "value": row["count"]} for row in location_stats], "tumor_type_stats": [{"name": row.tumor_type, "value": row["count"]} for row in tumor_type_stats], "stage_distribution": [{"name": row.tumor_stage, "value": row["count"]} for row in stage_distribution]}
return JsonResponse({"code": 200, "data": result, "message": "患者画像分析完成"})
class ClinicalFeatureAnalysis(View):
def post(self, request):
data = json.loads(request.body)
clinical_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/eyecancer").option("dbtable", "clinical_features").option("user", "root").option("password", "123456").load()
symptom_frequency = clinical_data.groupBy("main_symptom").count().orderBy(desc("count")).collect()
vision_impact = clinical_data.groupBy("vision_loss_degree").count().collect()
tumor_size_stats = clinical_data.select(avg("tumor_size").alias("avg_size"), max("tumor_size").alias("max_size"), min("tumor_size").alias("min_size")).collect()[0]
intraocular_pressure = clinical_data.select(avg("intraocular_pressure").alias("avg_pressure"), max("intraocular_pressure").alias("max_pressure"), min("intraocular_pressure").alias("min_pressure")).collect()[0]
lymph_node_stats = clinical_data.groupBy("lymph_node_involvement").count().collect()
metastasis_stats = clinical_data.groupBy("metastasis_status").count().collect()
biomarker_analysis = clinical_data.groupBy("biomarker_expression").count().collect()
histological_type = clinical_data.groupBy("histological_type").count().collect()
grade_distribution = clinical_data.groupBy("tumor_grade").count().collect()
laterality_stats = clinical_data.groupBy("laterality").count().collect()
quadrant_location = clinical_data.groupBy("quadrant_location").count().collect()
complications = clinical_data.groupBy("complications").count().orderBy(desc("count")).collect()
diagnostic_method = clinical_data.groupBy("diagnostic_method").count().collect()
imaging_findings = clinical_data.groupBy("imaging_findings").count().collect()
pathology_results = clinical_data.groupBy("pathology_result").count().collect()
correlation_analysis = clinical_data.stat.corr("tumor_size", "intraocular_pressure")
result = {"symptom_frequency": [{"name": row.main_symptom, "value": row["count"]} for row in symptom_frequency], "vision_impact": [{"name": row.vision_loss_degree, "value": row["count"]} for row in vision_impact], "tumor_size_stats": {"avg": float(tumor_size_stats.avg_size), "max": float(tumor_size_stats.max_size), "min": float(tumor_size_stats.min_size)}, "pressure_stats": {"avg": float(intraocular_pressure.avg_pressure), "max": float(intraocular_pressure.max_pressure), "min": float(intraocular_pressure.min_pressure)}, "correlation": float(correlation_analysis)}
return JsonResponse({"code": 200, "data": result, "message": "临床特征分析完成"})
class TreatmentEffectAnalysis(View):
def post(self, request):
data = json.loads(request.body)
treatment_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/eyecancer").option("dbtable", "treatment_records").option("user", "root").option("password", "123456").load()
treatment_methods = treatment_data.groupBy("treatment_method").count().orderBy(desc("count")).collect()
success_rates = treatment_data.groupBy("treatment_method").agg((sum(when(col("treatment_outcome") == "成功", 1).otherwise(0)) / count("*")).alias("success_rate")).collect()
surgery_outcomes = treatment_data.filter(col("treatment_method").contains("手术")).groupBy("treatment_outcome").count().collect()
chemotherapy_effects = treatment_data.filter(col("treatment_method").contains("化疗")).groupBy("side_effects").count().collect()
radiotherapy_response = treatment_data.filter(col("treatment_method").contains("放疗")).groupBy("treatment_response").count().collect()
combination_therapy = treatment_data.filter(col("treatment_method").contains("联合")).groupBy("treatment_outcome").count().collect()
treatment_duration = treatment_data.groupBy("treatment_method").agg(avg("treatment_duration").alias("avg_duration")).collect()
hospitalization_time = treatment_data.groupBy("treatment_method").agg(avg("hospitalization_days").alias("avg_days")).collect()
cost_analysis = treatment_data.groupBy("treatment_method").agg(avg("treatment_cost").alias("avg_cost")).collect()
recurrence_rates = treatment_data.groupBy("treatment_method").agg((sum(when(col("recurrence_status") == "复发", 1).otherwise(0)) / count("*")).alias("recurrence_rate")).collect()
complication_rates = treatment_data.groupBy("treatment_method").agg((sum(when(col("complications").isNotNull(), 1).otherwise(0)) / count("*")).alias("complication_rate")).collect()
quality_of_life = treatment_data.groupBy("treatment_method").agg(avg("quality_score").alias("avg_quality")).collect()
patient_satisfaction = treatment_data.groupBy("treatment_method").agg(avg("satisfaction_score").alias("avg_satisfaction")).collect()
follow_up_results = treatment_data.groupBy("treatment_method", "follow_up_result").count().collect()
age_treatment_correlation = treatment_data.groupBy("treatment_method").agg(avg("patient_age").alias("avg_age")).collect()
stage_treatment_distribution = treatment_data.groupBy("tumor_stage", "treatment_method").count().collect()
result = {"treatment_methods": [{"name": row.treatment_method, "value": row["count"]} for row in treatment_methods], "success_rates": [{"name": row.treatment_method, "value": float(row.success_rate)} for row in success_rates], "surgery_outcomes": [{"name": row.treatment_outcome, "value": row["count"]} for row in surgery_outcomes], "duration_stats": [{"name": row.treatment_method, "value": float(row.avg_duration)} for row in treatment_duration], "cost_analysis": [{"name": row.treatment_method, "value": float(row.avg_cost)} for row in cost_analysis]}
return JsonResponse({"code": 200, "data": result, "message": "治疗方案效果分析完成"})
基于大数据的眼癌数据分析与可视化系统文档展示
💖💖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
