💖💖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
基于大数据技术的医疗数据分析与研究介绍
基于Spark的医疗大数据分析系统是一个集数据采集、处理、分析和可视化于一体的综合性医疗数据研究平台。该系统充分利用Apache Spark强大的分布式计算能力，结合Hadoop生态系统的HDFS存储优势，实现对海量医疗数据的高效处理和深度挖掘。系统采用Python作为主要开发语言，Django作为后端开发框架，前端使用Vue.js配合ElementUI构建用户界面，通过Echarts实现数据的动态可视化展示。在数据处理层面，系统集成了Spark SQL进行结构化数据查询，利用Pandas和NumPy进行数据预处理和统计分析，同时支持多种医疗数据格式的导入和解析。系统设计了完整的用户管理体系，包含用户注册登录、权限控制和个人信息管理功能。核心业务模块围绕医疗数据管理和预测分析展开，支持医疗记录的批量导入、数据清洗、统计分析以及基于历史数据的趋势预测。整个系统架构采用前后端分离的设计模式，确保了良好的扩展性和维护性，为医疗数据的科学研究提供了可靠的技术支撑平台。
基于大数据技术的医疗数据分析与研究演示视频
基于大数据技术的医疗数据分析与研究演示图片
基于大数据技术的医疗数据分析与研究代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, count, max, min, sum, when, isnan, isnull
from pyspark.ml.feature import VectorAssembler, StandardScaler
from pyspark.ml.regression import LinearRegression
from pyspark.ml.evaluation import RegressionEvaluator
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("MedicalDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def medical_data_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
file_path = data.get('file_path')
analysis_type = data.get('analysis_type')
df = spark.read.option("header", "true").option("inferSchema", "true").csv(file_path)
df = df.filter(~(col("age").isNull() | col("blood_pressure").isNull() | col("heart_rate").isNull()))
df = df.filter((col("age") >= 0) & (col("age") <= 120))
df = df.filter((col("blood_pressure") >= 50) & (col("blood_pressure") <= 300))
df = df.filter((col("heart_rate") >= 40) & (col("heart_rate") <= 200))
if analysis_type == "basic_stats":
stats_result = df.select(avg("age").alias("avg_age"), avg("blood_pressure").alias("avg_bp"), avg("heart_rate").alias("avg_hr"), count("*").alias("total_records"), max("age").alias("max_age"), min("age").alias("min_age")).collect()[0]
age_groups = df.select(when(col("age") < 18, "child").when((col("age") >= 18) & (col("age") < 65), "adult").otherwise("elderly").alias("age_group")).groupBy("age_group").count()
disease_stats = df.groupBy("diagnosis").count().orderBy(col("count").desc())
result_data = {"basic_stats": stats_result.asDict(), "age_distribution": [row.asDict() for row in age_groups.collect()], "disease_distribution": [row.asDict() for row in disease_stats.collect()]}
elif analysis_type == "correlation":
correlation_matrix = df.select("age", "blood_pressure", "heart_rate", "cholesterol").toPandas().corr()
high_risk_patients = df.filter((col("blood_pressure") > 140) | (col("cholesterol") > 240) | (col("heart_rate") > 100)).count()
risk_analysis = df.select(when((col("blood_pressure") > 140) | (col("cholesterol") > 240), "high_risk").when((col("blood_pressure") > 120) | (col("cholesterol") > 200), "medium_risk").otherwise("low_risk").alias("risk_level")).groupBy("risk_level").count()
result_data = {"correlation_matrix": correlation_matrix.to_dict(), "high_risk_count": high_risk_patients, "risk_distribution": [row.asDict() for row in risk_analysis.collect()]}
return JsonResponse({"status": "success", "data": result_data})
return JsonResponse({"status": "error", "message": "Invalid request method"})
@csrf_exempt
def medical_prediction_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
training_data_path = data.get('training_data_path')
prediction_features = data.get('features')
target_column = data.get('target', 'risk_score')
df = spark.read.option("header", "true").option("inferSchema", "true").csv(training_data_path)
df = df.filter(~(col("age").isNull() | col("blood_pressure").isNull() | col("heart_rate").isNull() | col("cholesterol").isNull()))
feature_columns = ["age", "blood_pressure", "heart_rate", "cholesterol", "bmi"]
assembler = VectorAssembler(inputCols=feature_columns, outputCol="features")
df_assembled = assembler.transform(df)
scaler = StandardScaler(inputCol="features", outputCol="scaled_features")
scaler_model = scaler.fit(df_assembled)
df_scaled = scaler_model.transform(df_assembled)
train_data, test_data = df_scaled.randomSplit([0.8, 0.2], seed=42)
lr = LinearRegression(featuresCol="scaled_features", labelCol=target_column)
lr_model = lr.fit(train_data)
predictions = lr_model.transform(test_data)
evaluator = RegressionEvaluator(labelCol=target_column, predictionCol="prediction", metricName="rmse")
rmse = evaluator.evaluate(predictions)
mae_evaluator = RegressionEvaluator(labelCol=target_column, predictionCol="prediction", metricName="mae")
mae = mae_evaluator.evaluate(predictions)
r2_evaluator = RegressionEvaluator(labelCol=target_column, predictionCol="prediction", metricName="r2")
r2 = r2_evaluator.evaluate(predictions)
feature_importance = [(feature_columns[i], float(lr_model.coefficients[i])) for i in range(len(feature_columns))]
if prediction_features:
prediction_df = spark.createDataFrame([prediction_features], feature_columns)
prediction_assembled = assembler.transform(prediction_df)
prediction_scaled = scaler_model.transform(prediction_assembled)
prediction_result = lr_model.transform(prediction_scaled)
predicted_value = prediction_result.select("prediction").collect()[0]["prediction"]
else:
predicted_value = None
result_data = {"model_performance": {"rmse": rmse, "mae": mae, "r2": r2}, "feature_importance": feature_importance, "prediction": predicted_value}
return JsonResponse({"status": "success", "data": result_data})
return JsonResponse({"status": "error", "message": "Invalid request method"})
@csrf_exempt
def user_data_management(request):
if request.method == 'POST':
data = json.loads(request.body)
operation = data.get('operation')
user_data_path = data.get('user_data_path')
if operation == "profile_analysis":
df = spark.read.option("header", "true").option("inferSchema", "true").csv(user_data_path)
df = df.filter(col("user_id").isNotNull() & col("registration_date").isNotNull())
total_users = df.count()
active_users = df.filter(col("last_login_date") >= "2024-01-01").count()
user_age_stats = df.select(avg("age").alias("avg_age"), max("age").alias("max_age"), min("age").alias("min_age"))
gender_distribution = df.groupBy("gender").count()
registration_trend = df.select(col("registration_date").substr(1, 7).alias("month")).groupBy("month").count().orderBy("month")
login_frequency = df.select(when(col("login_count") > 50, "high_frequency").when(col("login_count") > 10, "medium_frequency").otherwise("low_frequency").alias("frequency_type")).groupBy("frequency_type").count()
user_location = df.groupBy("city").count().orderBy(col("count").desc()).limit(10)
monthly_new_users = df.filter(col("registration_date") >= "2024-01-01").select(col("registration_date").substr(1, 7).alias("month")).groupBy("month").count()
user_retention = df.filter((col("registration_date") < "2024-06-01") & (col("last_login_date") >= "2024-06-01")).count()
total_old_users = df.filter(col("registration_date") < "2024-06-01").count()
retention_rate = (user_retention / total_old_users * 100) if total_old_users > 0 else 0
average_session_time = df.select(avg("avg_session_duration").alias("avg_session")).collect()[0]["avg_session"]
result_data = {"total_users": total_users, "active_users": active_users, "age_stats": user_age_stats.collect()[0].asDict(), "gender_distribution": [row.asDict() for row in gender_distribution.collect()], "registration_trend": [row.asDict() for row in registration_trend.collect()], "login_frequency": [row.asDict() for row in login_frequency.collect()], "top_cities": [row.asDict() for row in user_location.collect()], "monthly_growth": [row.asDict() for row in monthly_new_users.collect()], "retention_rate": retention_rate, "avg_session_time": average_session_time}
elif operation == "behavior_analysis":
df = spark.read.option("header", "true").option("inferSchema", "true").csv(user_data_path)
page_views = df.groupBy("page_visited").count().orderBy(col("count").desc())
peak_hours = df.select(col("login_time").substr(12, 2).alias("hour")).groupBy("hour").count().orderBy(col("count").desc())
device_usage = df.groupBy("device_type").count()
feature_usage = df.select(sum("medical_data_views").alias("total_medical_views"), sum("prediction_requests").alias("total_predictions"), avg("session_duration").alias("avg_duration"))
user_segments = df.select(when(col("total_actions") > 100, "power_user").when(col("total_actions") > 20, "regular_user").otherwise("casual_user").alias("user_segment")).groupBy("user_segment").count()
result_data = {"popular_pages": [row.asDict() for row in page_views.collect()], "peak_usage_hours": [row.asDict() for row in peak_hours.collect()], "device_distribution": [row.asDict() for row in device_usage.collect()], "feature_statistics": feature_usage.collect()[0].asDict(), "user_segments": [row.asDict() for row in user_segments.collect()]}
return JsonResponse({"status": "success", "data": result_data})
return JsonResponse({"status": "error", "message": "Invalid request method"})
基于大数据技术的医疗数据分析与研究文档展示
💖💖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
