💖💖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
基于大数据的小儿阑尾炎数据分析系统介绍
小儿阑尾炎数据分析系统是一套基于Hadoop+Spark大数据技术架构的医疗数据智能分析平台，专门针对小儿阑尾炎临床数据进行深度挖掘和分析处理。系统采用Python作为核心开发语言，后端使用Django框架构建RESTful API接口，前端基于Vue+ElementUI+Echarts技术栈打造现代化的可视化交互界面。系统集成了患者特征分析、核心因素分析、病情严重度分析、临床决策分析四大核心分析模块，通过Spark SQL对海量医疗数据进行实时处理，利用Pandas和NumPy进行数据预处理和统计计算，最终以直观的图表形式展现分析结果。系统还提供用户管理、数据管理、系统公告等基础功能模块，确保整个平台的稳定运行和数据安全。通过HDFS分布式文件系统存储医疗数据，保证了系统在处理大规模数据时的高效性和可扩展性，为医疗机构提供了一套完整的小儿阑尾炎数据分析解决方案。
基于大数据的小儿阑尾炎数据分析系统演示视频
基于大数据的小儿阑尾炎数据分析系统演示图片
基于大数据的小儿阑尾炎数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
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("PediatricAppendicitisAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def patient_feature_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
age_range = data.get('age_range', [0, 18])
gender_filter = data.get('gender', 'all')
symptom_types = data.get('symptom_types', [])
patient_df = spark.sql("SELECT * FROM pediatric_patients WHERE age >= {} AND age <= {}".format(age_range[0], age_range[1]))
if gender_filter != 'all':
patient_df = patient_df.filter(col('gender') == gender_filter)
if symptom_types:
symptom_condition = " OR ".join([f"symptoms LIKE '%{symptom}%'" for symptom in symptom_types])
patient_df = patient_df.filter(expr(symptom_condition))
age_distribution = patient_df.groupBy('age_group').agg(count('patient_id').alias('count'), avg('pain_score').alias('avg_pain'), max('temperature').alias('max_temp')).collect()
symptom_correlation = patient_df.select('vomiting', 'nausea', 'fever', 'abdominal_pain', 'diagnosis_result').toPandas()
correlation_matrix = symptom_correlation.corr().to_dict()
gender_stats = patient_df.groupBy('gender').agg(count('patient_id').alias('total_cases'), avg('hospitalization_days').alias('avg_days'), sum(when(col('diagnosis_result') == 'positive', 1).otherwise(0)).alias('positive_cases')).collect()
pain_severity_analysis = patient_df.withColumn('pain_level', when(col('pain_score') >= 8, 'severe').when(col('pain_score') >= 5, 'moderate').otherwise('mild')).groupBy('pain_level').agg(count('patient_id').alias('count'), avg('white_blood_cell').alias('avg_wbc')).collect()
result_data = {'age_distribution': [{'age_group': row['age_group'], 'count': row['count'], 'avg_pain': float(row['avg_pain']), 'max_temp': float(row['max_temp'])} for row in age_distribution], 'symptom_correlation': correlation_matrix, 'gender_stats': [{'gender': row['gender'], 'total_cases': row['total_cases'], 'avg_days': float(row['avg_days']), 'positive_rate': row['positive_cases']/row['total_cases']*100} for row in gender_stats], 'pain_analysis': [{'pain_level': row['pain_level'], 'count': row['count'], 'avg_wbc': float(row['avg_wbc'])} for row in pain_severity_analysis]}
return JsonResponse({'status': 'success', 'data': result_data})
@csrf_exempt
def core_factor_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
analysis_period = data.get('period', 'last_year')
factor_types = data.get('factors', ['lab_results', 'symptoms', 'vital_signs'])
time_condition = "created_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 1 YEAR)" if analysis_period == 'last_year' else "created_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 6 MONTH)"
clinical_data = spark.sql(f"SELECT * FROM clinical_records WHERE {time_condition}")
lab_factor_analysis = clinical_data.select('white_blood_cell', 'c_reactive_protein', 'neutrophil_count', 'diagnosis_result').toPandas()
lab_correlation = lab_factor_analysis.corr()['diagnosis_result'].drop('diagnosis_result').to_dict()
symptom_importance = clinical_data.groupBy('primary_symptom').agg(count('patient_id').alias('frequency'), avg('diagnosis_accuracy').alias('avg_accuracy'), sum(when(col('diagnosis_result') == 1, 1).otherwise(0)).alias('positive_predictions')).orderBy(desc('avg_accuracy')).collect()
vital_signs_impact = clinical_data.select('temperature', 'heart_rate', 'blood_pressure', 'respiratory_rate', 'diagnosis_result').withColumn('temp_abnormal', when(col('temperature') > 38.5, 1).otherwise(0)).withColumn('hr_abnormal', when(col('heart_rate') > 120, 1).otherwise(0)).groupBy('temp_abnormal', 'hr_abnormal').agg(count('patient_id').alias('case_count'), avg('diagnosis_result').alias('positive_rate')).collect()
risk_score_calculation = clinical_data.withColumn('risk_score', col('white_blood_cell')*0.3 + col('c_reactive_protein')*0.2 + col('temperature')*0.15 + col('pain_score')*0.35).withColumn('risk_level', when(col('risk_score') >= 7, 'high').when(col('risk_score') >= 4, 'medium').otherwise('low')).groupBy('risk_level').agg(count('patient_id').alias('count'), avg('diagnosis_result').alias('accuracy')).collect()
factor_weights = clinical_data.select('white_blood_cell', 'c_reactive_protein', 'pain_score', 'temperature', 'vomiting_frequency').toPandas()
weights_std = factor_weights.std().to_dict()
normalized_weights = {k: v/sum(weights_std.values()) for k, v in weights_std.items()}
response_data = {'lab_correlations': lab_correlation, 'symptom_ranking': [{'symptom': row['primary_symptom'], 'frequency': row['frequency'], 'accuracy': float(row['avg_accuracy']), 'positive_rate': row['positive_predictions']/row['frequency']*100} for row in symptom_importance], 'vital_impact': [{'temp_abnormal': row['temp_abnormal'], 'hr_abnormal': row['hr_abnormal'], 'case_count': row['case_count'], 'positive_rate': float(row['positive_rate'])*100} for row in vital_signs_impact], 'risk_distribution': [{'risk_level': row['risk_level'], 'count': row['count'], 'accuracy': float(row['accuracy'])*100} for row in risk_score_calculation], 'factor_weights': normalized_weights}
return JsonResponse({'status': 'success', 'analysis_result': response_data})
@csrf_exempt
def severity_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
patient_ids = data.get('patient_ids', [])
analysis_type = data.get('type', 'comprehensive')
severity_features = data.get('features', ['pain_score', 'lab_values', 'imaging_results'])
patient_filter = f"patient_id IN ({','.join(map(str, patient_ids))})" if patient_ids else "1=1"
severity_data = spark.sql(f"SELECT * FROM patient_severity_records WHERE {patient_filter}")
pain_severity_mapping = severity_data.withColumn('severity_score', when(col('pain_score') >= 9, 4).when(col('pain_score') >= 7, 3).when(col('pain_score') >= 4, 2).otherwise(1)).select('patient_id', 'pain_score', 'severity_score', 'white_blood_cell', 'c_reactive_protein', 'ultrasound_findings')
lab_severity_indicators = pain_severity_mapping.withColumn('wbc_severity', when(col('white_blood_cell') > 15000, 'severe').when(col('white_blood_cell') > 12000, 'moderate').otherwise('mild')).withColumn('crp_severity', when(col('c_reactive_protein') > 100, 'severe').when(col('c_reactive_protein') > 50, 'moderate').otherwise('mild'))
combined_severity = lab_severity_indicators.withColumn('overall_severity', when((col('severity_score') >= 3) & (col('wbc_severity') == 'severe') & (col('crp_severity') == 'severe'), 'critical').when((col('severity_score') >= 2) & ((col('wbc_severity') == 'severe') | (col('crp_severity') == 'severe')), 'high').when(col('severity_score') >= 2, 'moderate').otherwise('low'))
severity_distribution = combined_severity.groupBy('overall_severity').agg(count('patient_id').alias('patient_count'), avg('pain_score').alias('avg_pain'), avg('white_blood_cell').alias('avg_wbc'), avg('c_reactive_protein').alias('avg_crp')).collect()
progression_analysis = severity_data.withColumn('day_diff', datediff(col('discharge_date'), col('admission_date'))).groupBy('overall_severity').agg(avg('day_diff').alias('avg_stay'), sum(when(col('complications') == 1, 1).otherwise(0)).alias('complication_count'), count('patient_id').alias('total_cases')).collect()
treatment_outcomes = combined_severity.join(spark.sql("SELECT patient_id, treatment_type, outcome_score, recovery_days FROM treatment_outcomes"), 'patient_id').groupBy('overall_severity', 'treatment_type').agg(avg('outcome_score').alias('avg_outcome'), avg('recovery_days').alias('avg_recovery')).collect()
risk_prediction_model = combined_severity.select('severity_score', 'white_blood_cell', 'c_reactive_protein', 'pain_score').toPandas()
risk_coefficients = np.polyfit(risk_prediction_model['severity_score'], risk_prediction_model['white_blood_cell'], 2).tolist()
severity_trends = severity_data.withColumn('admission_month', month(col('admission_date'))).groupBy('admission_month', 'overall_severity').agg(count('patient_id').alias('monthly_cases')).orderBy('admission_month').collect()
analysis_results = {'severity_distribution': [{'severity': row['overall_severity'], 'count': row['patient_count'], 'avg_pain': float(row['avg_pain']), 'avg_wbc': float(row['avg_wbc']), 'avg_crp': float(row['avg_crp'])} for row in severity_distribution], 'progression_data': [{'severity': row['overall_severity'], 'avg_stay': float(row['avg_stay']), 'complication_rate': row['complication_count']/row['total_cases']*100} for row in progression_analysis], 'treatment_effectiveness': [{'severity': row['overall_severity'], 'treatment': row['treatment_type'], 'outcome_score': float(row['avg_outcome']), 'recovery_days': float(row['avg_recovery'])} for row in treatment_outcomes], 'risk_model_coefficients': risk_coefficients, 'monthly_trends': [{'month': row['admission_month'], 'severity': row['overall_severity'], 'cases': row['monthly_cases']} for row in severity_trends]}
return JsonResponse({'status': 'success', 'severity_analysis': analysis_results})
基于大数据的小儿阑尾炎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
