💖💖作者：计算机编程小咖 💙💙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
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痴呆症预测数据可视化分析系统介绍
基于大数据的痴呆症预测数据可视化分析系统是一个集数据处理、分析预测与可视化展示于一体的综合性医疗数据分析平台。该系统采用先进的大数据技术架构，以Hadoop分布式文件系统（HDFS）作为数据存储基础，结合Spark大数据计算框架进行高效的数据处理和分析，通过Spark SQL实现复杂的数据查询和统计分析功能。系统后端基于Python语言开发，采用Django框架构建稳定的服务端架构，同时集成Pandas和NumPy等专业数据分析库，为痴呆症相关医疗数据提供强大的数据处理能力。前端采用Vue.js框架结合ElementUI组件库构建现代化的用户界面，通过Echarts图表库实现丰富的数据可视化效果，配合HTML、CSS、JavaScript和jQuery技术栈，为用户提供流畅的交互体验。系统功能涵盖完整的数据管理流程，包括用户权限管理、个人信息维护、痴呆症数据的录入与管理，以及专业的数据分析模块如脑影像分析、临床特征分析、人群特征分析和纵向追踪分析等核心功能。特别设计的可视化大屏模块能够直观展示数据分析结果，通过MySQL数据库确保数据的安全存储和高效检索，为医疗研究人员和相关专业人士提供了一个功能全面、技术先进的痴呆症数据分析工具。
痴呆症预测数据可视化分析系统演示视频
痴呆症预测数据可视化分析系统演示图片
痴呆症预测数据可视化分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, when, count, avg, sum, max, min, desc, asc
from pyspark.ml.feature import VectorAssembler, StandardScaler
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.evaluation import BinaryClassificationEvaluator
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("DementiaAnalysisSystem").config("spark.some.config.option", "some-value").getOrCreate()
@csrf_exempt
def dementia_data_prediction(request):
if request.method == 'POST':
data = json.loads(request.body)
patient_data = data.get('patient_data')
df = spark.createDataFrame(patient_data)
feature_cols = ['age', 'education_years', 'mmse_score', 'brain_volume', 'hippocampus_volume']
assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
feature_data = assembler.transform(df)
scaler = StandardScaler(inputCol="features", outputCol="scaled_features")
scaler_model = scaler.fit(feature_data)
scaled_data = scaler_model.transform(feature_data)
rf = RandomForestClassifier(featuresCol="scaled_features", labelCol="dementia_label", numTrees=100)
model = rf.fit(scaled_data)
predictions = model.transform(scaled_data)
prediction_results = predictions.select("patient_id", "prediction", "probability").collect()
risk_levels = []
for row in prediction_results:
prob_array = row.probability.toArray()
risk_score = prob_array[1]
if risk_score > 0.8:
risk_level = "高风险"
elif risk_score > 0.5:
risk_level = "中等风险"
else:
risk_level = "低风险"
risk_levels.append({
'patient_id': row.patient_id,
'prediction': int(row.prediction),
'risk_score': float(risk_score),
'risk_level': risk_level
})
evaluator = BinaryClassificationEvaluator(labelCol="dementia_label", rawPredictionCol="rawPrediction")
accuracy = evaluator.evaluate(predictions)
return JsonResponse({
'status': 'success',
'predictions': risk_levels,
'model_accuracy': float(accuracy),
'total_patients': len(prediction_results)
})
@csrf_exempt
def brain_image_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
brain_image_data = data.get('brain_images')
df = spark.createDataFrame(brain_image_data)
brain_regions = ['frontal_lobe', 'parietal_lobe', 'temporal_lobe', 'occipital_lobe', 'hippocampus', 'amygdala']
volume_analysis = df.agg(
avg('total_brain_volume').alias('avg_brain_volume'),
avg('gray_matter_volume').alias('avg_gray_matter'),
avg('white_matter_volume').alias('avg_white_matter'),
avg('hippocampus_volume').alias('avg_hippocampus')
).collect()[0]
atrophy_detection = df.withColumn(
'brain_atrophy_level',
when(col('total_brain_volume') < 1200, 'severe')
.when(col('total_brain_volume') < 1400, 'moderate')
.otherwise('mild')
)
atrophy_stats = atrophy_detection.groupBy('brain_atrophy_level').count().collect()
region_comparison = df.select('patient_id', *brain_regions)
abnormal_regions = []
for region in brain_regions:
region_stats = df.agg(avg(region).alias('avg'), min(region).alias('min'), max(region).alias('max')).collect()[0]
threshold = region_stats['avg'] * 0.7
abnormal_count = df.filter(col(region) < threshold).count()
abnormal_regions.append({
'region': region,
'average_volume': float(region_stats['avg']),
'abnormal_count': abnormal_count,
'abnormal_rate': round(abnormal_count / df.count() * 100, 2)
})
longitudinal_data = df.filter(col('scan_date').isNotNull()).orderBy('patient_id', 'scan_date')
volume_trends = longitudinal_data.groupBy('patient_id').agg(
count('scan_date').alias('scan_count'),
(max('total_brain_volume') - min('total_brain_volume')).alias('volume_change')
).filter(col('scan_count') > 1).collect()
trend_analysis = []
for row in volume_trends:
change_rate = float(row.volume_change) / row.scan_count
trend_analysis.append({
'patient_id': row.patient_id,
'volume_change': float(row.volume_change),
'change_rate': change_rate,
'progression_status': 'declining' if change_rate < -10 else 'stable'
})
return JsonResponse({
'status': 'success',
'volume_analysis': {
'avg_brain_volume': float(volume_analysis['avg_brain_volume']),
'avg_gray_matter': float(volume_analysis['avg_gray_matter']),
'avg_white_matter': float(volume_analysis['avg_white_matter']),
'avg_hippocampus': float(volume_analysis['avg_hippocampus'])
},
'atrophy_distribution': [{'level': row.brain_atrophy_level, 'count': row.count} for row in atrophy_stats],
'abnormal_regions': abnormal_regions,
'trend_analysis': trend_analysis
})
@csrf_exempt
def clinical_feature_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
clinical_data = data.get('clinical_features')
df = spark.createDataFrame(clinical_data)
cognitive_scores = ['mmse_score', 'moca_score', 'cdr_score', 'adas_score']
score_statistics = df.agg(
avg('mmse_score').alias('avg_mmse'),
avg('moca_score').alias('avg_moca'),
avg('cdr_score').alias('avg_cdr'),
count(when(col('mmse_score') < 24, True)).alias('mmse_impaired_count'),
count(when(col('moca_score') < 26, True)).alias('moca_impaired_count')
).collect()[0]
risk_factor_analysis = df.withColumn(
'hypertension_risk',
when(col('systolic_bp') > 140, 'high').otherwise('normal')
).withColumn(
'diabetes_risk',
when(col('fasting_glucose') > 126, 'diabetic')
.when(col('fasting_glucose') > 100, 'prediabetic')
.otherwise('normal')
).withColumn(
'cholesterol_risk',
when(col('total_cholesterol') > 240, 'high').otherwise('normal')
)
risk_distribution = risk_factor_analysis.groupBy('hypertension_risk').count().collect()
diabetes_distribution = risk_factor_analysis.groupBy('diabetes_risk').count().collect()
cholesterol_distribution = risk_factor_analysis.groupBy('cholesterol_risk').count().collect()
biomarker_analysis = df.filter(col('amyloid_beta').isNotNull() & col('tau_protein').isNotNull())
biomarker_correlation = biomarker_analysis.agg(
avg('amyloid_beta').alias('avg_amyloid'),
avg('tau_protein').alias('avg_tau'),
avg('neurofilament').alias('avg_neurofilament'),
count(when(col('amyloid_beta') > 200, True)).alias('high_amyloid_count'),
count(when(col('tau_protein') > 80, True)).alias('high_tau_count')
).collect()[0]
symptom_severity = df.withColumn(
'cognitive_impairment_level',
when((col('mmse_score') < 20) & (col('moca_score') < 20), 'severe')
.when((col('mmse_score') < 24) | (col('moca_score') < 26), 'mild')
.otherwise('normal')
)
severity_stats = symptom_severity.groupBy('cognitive_impairment_level').count().collect()
medication_effectiveness = df.filter(col('medication_type').isNotNull())
med_analysis = medication_effectiveness.groupBy('medication_type').agg(
avg('mmse_score').alias('avg_mmse_score'),
count('patient_id').alias('patient_count')
).collect()
return JsonResponse({
'status': 'success',
'cognitive_assessment': {
'avg_mmse': float(score_statistics['avg_mmse']),
'avg_moca': float(score_statistics['avg_moca']),
'avg_cdr': float(score_statistics['avg_cdr']),
'mmse_impaired_rate': round(score_statistics['mmse_impaired_count'] / df.count() * 100, 2),
'moca_impaired_rate': round(score_statistics['moca_impaired_count'] / df.count() * 100, 2)
},
'risk_factors': {
'hypertension': [{'risk_level': row.hypertension_risk, 'count': row.count} for row in risk_distribution],
'diabetes': [{'risk_level': row.diabetes_risk, 'count': row.count} for row in diabetes_distribution],
'cholesterol': [{'risk_level': row.cholesterol_risk, 'count': row.count} for row in cholesterol_distribution]
},
'biomarkers': {
'avg_amyloid': float(biomarker_correlation['avg_amyloid']),
'avg_tau': float(biomarker_correlation['avg_tau']),
'high_amyloid_rate': round(biomarker_correlation['high_amyloid_count'] / biomarker_analysis.count() * 100, 2),
'high_tau_rate': round(biomarker_correlation['high_tau_count'] / biomarker_analysis.count() * 100, 2)
},
'severity_distribution': [{'level': row.cognitive_impairment_level, 'count': row.count} for row in severity_stats],
'medication_analysis': [{'medication': row.medication_type, 'avg_score': float(row.avg_mmse_score), 'patient_count': row.patient_count} for row in med_analysis]
})
痴呆症预测数据可视化分析系统文档展示
💖💖作者：计算机编程小咖 💙💙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
