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基于大数据的眼癌数据分析与可视化系统-功能介绍
《基于大数据的眼癌数据分析与可视化系统》是一个专门针对眼癌医疗数据进行深度挖掘和可视化展示的综合性分析平台。系统采用Hadoop+Spark大数据处理架构作为核心技术底座，通过Spark SQL和Pandas、NumPy等数据科学工具对海量眼癌患者的医疗记录进行多维度统计分析。系统从患者基本特征与疾病分布、癌症诊断与临床特征、治疗方案与预后效果、高危因素与生存质量四个维度构建分析模型，涵盖年龄分布分析、性别差异分析、全球地域分布、诊断年份趋势、癌症类型分布、诊断分期构成、患病部位特征、遗传因素关联、治疗选择偏好、预后状态关系、生存时间预测等十余项核心分析功能。系统前端采用Vue+ElementUI+Echarts技术栈构建交互式数据可视化界面，后端基于Django/Spring Boot框架提供稳定的数据服务支撑，通过MySQL数据库存储处理结果，最终形成一个集数据采集、清洗、分析、可视化、预测为一体的医疗大数据应用平台，为眼癌疾病的流行病学研究和临床决策提供数据支持。
基于大数据的眼癌数据分析与可视化系统-选题背景意义
选题背景 眼癌作为一类相对罕见但严重威胁患者视力和生命的恶性肿瘤，包括视网膜母细胞瘤、眼部黑色素瘤、眼部淋巴瘤等多种类型，其发病机制复杂、临床表现多样、治疗方案个体化程度高。传统的眼癌医疗数据分析往往局限于小样本的统计描述和简单的报表展示，难以深入挖掘隐藏在大量患者数据中的潜在规律和关联性信息。随着医疗信息化进程的推进和电子病历系统的普及，眼癌相关的患者基本信息、诊断记录、治疗方案、预后追踪等数据呈现出数据量大、维度复杂、时间跨度长的特点，这些海量医疗数据蕴含着丰富的临床价值和科研意义。现有的传统数据处理技术在面对这类多维度、大规模的医疗数据时，往往存在处理效率低下、分析深度不够、可视化效果有限等问题，无法充分发挥大数据在医疗领域的应用潜力，迫切需要借助现代大数据技术手段来构建更加智能化、系统化的眼癌数据分析平台。 选题意义 本课题在技术层面探索了Hadoop分布式存储和Spark分布式计算在医疗数据分析领域的实际应用，通过构建基于大数据技术栈的眼癌分析系统，验证了大数据技术在处理结构化医疗数据方面的可行性和有效性，为后续类似的医疗大数据项目提供了技术参考和实践经验。从应用价值角度来看，系统通过多维度的数据挖掘和关联分析，能够帮助医务工作者更好地理解眼癌疾病的流行病学特征、临床表现规律以及不同治疗方案的效果差异，为制定更加精准的诊疗策略提供数据支撑。系统的可视化功能将复杂的统计分析结果以直观的图表形式展现，降低了医疗专业人员理解和应用分析结果的门槛，提高了数据的实用性和可操作性。在教学科研方面，该系统为医学院校的学生和研究人员提供了一个实用的数据分析工具，可以支持眼科相关的教学案例分析和科研项目开展。同时，项目的完成过程也为计算机专业学生提供了一次将大数据理论知识与实际医疗应用相结合的实践机会，有助于培养跨学科的复合型技术人才。
基于大数据的眼癌数据分析与可视化系统-技术选型
大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 开发语言：Python+Java（两个版本都支持） 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
基于大数据的眼癌数据分析与可视化系统-视频展示
基于大数据的眼癌数据分析与可视化系统-图片展示
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 col, count, avg, sum, when, desc, asc
from pyspark.ml.feature import VectorAssembler, StringIndexer
from pyspark.ml.regression import RandomForestRegressor
from pyspark.ml.evaluation import RegressionEvaluator
import pandas as pd
import numpy as np
def init_spark():
spark = SparkSession.builder.appName("EyeCancerAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
return spark
def analyze_patient_characteristics(spark, data_path):
df = spark.read.option("header", "true").option("inferSchema", "true").csv(data_path)
age_distribution = df.groupBy("Age").agg(count("*").alias("patient_count")).orderBy("Age")
age_ranges = df.withColumn("age_group", when(col("Age") < 18, "儿童").when(col("Age") < 40, "青年").when(col("Age") < 60, "中年").otherwise("老年"))
age_group_stats = age_ranges.groupBy("age_group").agg(count("*").alias("count"), avg("Survival_Time_Months").alias("avg_survival"))
gender_cancer_cross = df.groupBy("Gender", "Cancer_Type").agg(count("*").alias("patient_count")).orderBy(desc("patient_count"))
country_distribution = df.groupBy("Country").agg(count("*").alias("case_count")).orderBy(desc("case_count"))
df_with_year = df.withColumn("diagnosis_year", col("Date_of_Diagnosis").substr(1, 4))
yearly_trends = df_with_year.groupBy("diagnosis_year").agg(count("*").alias("yearly_cases")).orderBy("diagnosis_year")
cancer_type_stats = df.groupBy("Cancer_Type").agg(count("*").alias("total_cases"), avg("Age").alias("avg_age"), avg("Survival_Time_Months").alias("avg_survival"))
stage_distribution = df.groupBy("Stage_at_Diagnosis").agg(count("*").alias("case_count")).orderBy("Stage_at_Diagnosis")
genetic_analysis = df.groupBy("Genetic_Markers", "Cancer_Type").agg(count("*").alias("marker_count"), avg("Survival_Time_Months").alias("avg_survival_time"))
laterality_analysis = df.groupBy("Laterality", "Cancer_Type").agg(count("*").alias("affected_cases")).orderBy(desc("affected_cases"))
comprehensive_stats = df.agg(count("*").alias("total_patients"), avg("Age").alias("overall_avg_age"), avg("Survival_Time_Months").alias("overall_avg_survival"))
results = {"age_distribution": age_distribution.collect(), "age_group_stats": age_group_stats.collect(), "gender_cancer_cross": gender_cancer_cross.collect(), "country_distribution": country_distribution.collect(), "yearly_trends": yearly_trends.collect(), "cancer_type_stats": cancer_type_stats.collect(), "stage_distribution": stage_distribution.collect(), "genetic_analysis": genetic_analysis.collect(), "laterality_analysis": laterality_analysis.collect(), "comprehensive_stats": comprehensive_stats.collect()}
return results
def predict_survival_time(spark, data_path):
df = spark.read.option("header", "true").option("inferSchema", "true").csv(data_path)
df_clean = df.filter(col("Survival_Time_Months").isNotNull() & (col("Survival_Time_Months") > 0))
gender_indexer = StringIndexer(inputCol="Gender", outputCol="gender_index").fit(df_clean)
cancer_indexer = StringIndexer(inputCol="Cancer_Type", outputCol="cancer_index").fit(df_clean)
stage_indexer = StringIndexer(inputCol="Stage_at_Diagnosis", outputCol="stage_index").fit(df_clean)
treatment_indexer = StringIndexer(inputCol="Treatment_Type", outputCol="treatment_index").fit(df_clean)
genetic_indexer = StringIndexer(inputCol="Genetic_Markers", outputCol="genetic_index").fit(df_clean)
family_indexer = StringIndexer(inputCol="Family_History", outputCol="family_index").fit(df_clean)
df_indexed = gender_indexer.transform(df_clean)
df_indexed = cancer_indexer.transform(df_indexed)
df_indexed = stage_indexer.transform(df_indexed)
df_indexed = treatment_indexer.transform(df_indexed)
df_indexed = genetic_indexer.transform(df_indexed)
df_indexed = family_indexer.transform(df_indexed)
feature_cols = ["Age", "gender_index", "cancer_index", "stage_index", "treatment_index", "genetic_index", "family_index"]
assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
df_features = assembler.transform(df_indexed)
train_data, test_data = df_features.randomSplit([0.8, 0.2], seed=42)
rf = RandomForestRegressor(featuresCol="features", labelCol="Survival_Time_Months", numTrees=100, maxDepth=10, seed=42)
rf_model = rf.fit(train_data)
predictions = rf_model.transform(test_data)
evaluator = RegressionEvaluator(labelCol="Survival_Time_Months", predictionCol="prediction", metricName="rmse")
rmse = evaluator.evaluate(predictions)
mae_evaluator = RegressionEvaluator(labelCol="Survival_Time_Months", predictionCol="prediction", metricName="mae")
mae = mae_evaluator.evaluate(predictions)
r2_evaluator = RegressionEvaluator(labelCol="Survival_Time_Months", predictionCol="prediction", metricName="r2")
r2 = r2_evaluator.evaluate(predictions)
feature_importance = rf_model.featureImportances.toArray()
importance_dict = dict(zip(feature_cols, feature_importance))
survival_by_cancer = df_clean.groupBy("Cancer_Type").agg(avg("Survival_Time_Months").alias("avg_survival"), count("*").alias("sample_count"))
survival_by_stage = df_clean.groupBy("Stage_at_Diagnosis").agg(avg("Survival_Time_Months").alias("avg_survival"), count("*").alias("sample_count"))
survival_by_treatment = df_clean.groupBy("Treatment_Type").agg(avg("Survival_Time_Months").alias("avg_survival"), count("*").alias("sample_count"))
prediction_results = {"model_performance": {"rmse": rmse, "mae": mae, "r2": r2}, "feature_importance": importance_dict, "survival_by_cancer": survival_by_cancer.collect(), "survival_by_stage": survival_by_stage.collect(), "survival_by_treatment": survival_by_treatment.collect(), "sample_predictions": predictions.select("Age", "Cancer_Type", "Stage_at_Diagnosis", "Survival_Time_Months", "prediction").limit(20).collect()}
return prediction_results, rf_model
def analyze_treatment_outcomes(spark, data_path):
df = spark.read.option("header", "true").option("inferSchema", "true").csv(data_path)
treatment_cancer_matrix = df.groupBy("Treatment_Type", "Cancer_Type").agg(count("*").alias("patient_count")).orderBy("Treatment_Type", desc("patient_count"))
treatment_stage_analysis = df.groupBy("Treatment_Type", "Stage_at_Diagnosis").agg(count("*").alias("treatment_count"), avg("Survival_Time_Months").alias("avg_survival"))
outcome_treatment_cross = df.groupBy("Treatment_Type", "Outcome_Status").agg(count("*").alias("outcome_count")).orderBy("Treatment_Type", "Outcome_Status")
surgery_radiation_combo = df.groupBy("Surgery_Status", "Radiation_Therapy").agg(count("*").alias("combo_count"), avg("Survival_Time_Months").alias("combo_survival"))
chemotherapy_effectiveness = df.filter(col("Chemotherapy") == "Yes").groupBy("Cancer_Type", "Stage_at_Diagnosis").agg(count("*").alias("chemo_cases"), avg("Survival_Time_Months").alias("chemo_survival"))
treatment_success_rate = df.groupBy("Treatment_Type").agg(count("*").alias("total_cases"), sum(when(col("Outcome_Status") == "Remission", 1).otherwise(0)).alias("success_cases"))
treatment_success_rate = treatment_success_rate.withColumn("success_rate", col("success_cases") / col("total_cases") * 100)
stage_specific_outcomes = df.groupBy("Stage_at_Diagnosis", "Treatment_Type").agg(count("*").alias("cases"), avg("Survival_Time_Months").alias("avg_survival"), sum(when(col("Outcome_Status") == "Remission", 1).otherwise(0)).alias("remission_count"))
genetic_treatment_response = df.groupBy("Genetic_Markers", "Treatment_Type").agg(count("*").alias("genetic_cases"), avg("Survival_Time_Months").alias("genetic_survival"))
age_treatment_correlation = df.withColumn("age_group", when(col("Age") < 30, "年轻").when(col("Age") < 50, "中年").otherwise("老年")).groupBy("age_group", "Treatment_Type").agg(count("*").alias("age_treatment_count"), avg("Survival_Time_Months").alias("age_survival"))
multi_treatment_analysis = df.withColumn("treatment_complexity", when((col("Surgery_Status") == "Yes") & (col("Radiation_Therapy") == "Yes") & (col("Chemotherapy") == "Yes"), "三联治疗").when(((col("Surgery_Status") == "Yes") & (col("Radiation_Therapy") == "Yes")) | ((col("Surgery_Status") == "Yes") & (col("Chemotherapy") == "Yes")) | ((col("Radiation_Therapy") == "Yes") & (col("Chemotherapy") == "Yes")), "双联治疗").otherwise("单一治疗"))
complexity_outcomes = multi_treatment_analysis.groupBy("treatment_complexity").agg(count("*").alias("complexity_count"), avg("Survival_Time_Months").alias("complexity_survival"), sum(when(col("Outcome_Status") == "Remission", 1).otherwise(0)).alias("complexity_success"))
treatment_duration_analysis = df.filter(col("Survival_Time_Months").isNotNull()).groupBy("Treatment_Type").agg(avg("Survival_Time_Months").alias("avg_treatment_duration"), count("*").alias("duration_sample_size"))
comprehensive_treatment_results = {"treatment_cancer_matrix": treatment_cancer_matrix.collect(), "treatment_stage_analysis": treatment_stage_analysis.collect(), "outcome_treatment_cross": outcome_treatment_cross.collect(), "surgery_radiation_combo": surgery_radiation_combo.collect(), "chemotherapy_effectiveness": chemotherapy_effectiveness.collect(), "treatment_success_rate": treatment_success_rate.collect(), "stage_specific_outcomes": stage_specific_outcomes.collect(), "genetic_treatment_response": genetic_treatment_response.collect(), "age_treatment_correlation": age_treatment_correlation.collect(), "complexity_outcomes": complexity_outcomes.collect(), "treatment_duration_analysis": treatment_duration_analysis.collect()}
return comprehensive_treatment_results
基于大数据的眼癌数据分析与可视化系统-结语
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