💖💖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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基于大数据的红斑鳞状疾病数据可视化分析系统介绍
《基于大数据的红斑鳞状疾病数据可视化分析系统》是一套融合了现代大数据技术与医疗数据分析的综合性系统，采用Hadoop分布式存储架构结合Spark大数据处理引擎作为核心技术框架，通过HDFS实现海量医疗数据的可靠存储，利用Spark SQL进行高效的数据查询与处理，并结合Pandas和NumPy进行深度数据分析计算。系统提供Python+Django和Java+Spring Boot两套完整的后端解决方案，前端采用Vue框架配合ElementUI组件库构建现代化用户界面，通过Echarts图表库实现丰富的数据可视化展示效果。系统核心功能涵盖疾病样本分布分析、患者年龄分布统计、家族遗传史影响评估、临床症状强度量化分析、组织病理学特征识别、年龄段临床症状关联分析、瘙痒程度与疾病严重度关联度计算、关键症状关联度挖掘、关键特征诊断价值评估、症状特征关联度热力图可视化、皮肤炎症模式识别、皮肤结构变化特征分析、患者聚类分组、特征重要性排序以及特定体征组合疾病预测等十五大分析模块，同时提供大屏可视化展示、系统管理、个人信息管理等辅助功能，通过MySQL数据库存储结构化医疗数据，为医疗工作者和研究人员提供全方位的红斑鳞状疾病数据分析工具，实现从原始医疗数据到深度分析结果的完整数据处理链条，有效提升疾病诊断的准确性和效率。
基于大数据的红斑鳞状疾病数据可视化分析系统演示视频
基于大数据的红斑鳞状疾病数据可视化分析系统演示图片
基于大数据的红斑鳞状疾病数据可视化分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, desc, corr, when
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.clustering import KMeans
from pyspark.ml.evaluation import ClusteringEvaluator
import pandas as pd
import numpy as np
from django.http import JsonResponse
spark = SparkSession.builder.appName("RedScalyDiseaseAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def analyze_disease_sample_distribution(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/disease_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "disease_samples").option("user", "root").option("password", "password").load()
disease_counts = df.groupBy("disease_type").agg(count("*").alias("sample_count")).orderBy(desc("sample_count"))
age_group_distribution = df.withColumn("age_group", when(col("age") < 18, "未成年").when((col("age") >= 18) & (col("age") < 35), "青年").when((col("age") >= 35) & (col("age") < 60), "中年").otherwise("老年")).groupBy("disease_type", "age_group").agg(count("*").alias("count"))
severity_distribution = df.groupBy("disease_type", "severity_level").agg(count("*").alias("count")).orderBy("disease_type", "severity_level")
gender_distribution = df.groupBy("disease_type", "gender").agg(count("*").alias("count"))
family_history_impact = df.groupBy("disease_type", "family_history").agg(count("*").alias("count"), (count("*") * 100.0 / df.count()).alias("percentage"))
regional_distribution = df.groupBy("disease_type", "region").agg(count("*").alias("count")).orderBy("disease_type", desc("count"))
seasonal_pattern = df.withColumn("season", when(col("diagnosis_month").isin([12, 1, 2]), "冬季").when(col("diagnosis_month").isin([3, 4, 5]), "春季").when(col("diagnosis_month").isin([6, 7, 8]), "夏季").otherwise("秋季")).groupBy("disease_type", "season").agg(count("*").alias("count"))
treatment_response = df.groupBy("disease_type", "treatment_response").agg(count("*").alias("count"), (count("*") * 100.0 / df.count()).alias("response_rate"))
disease_counts_pandas = disease_counts.toPandas()
age_group_pandas = age_group_distribution.toPandas()
severity_pandas = severity_distribution.toPandas()
gender_pandas = gender_distribution.toPandas()
family_pandas = family_history_impact.toPandas()
regional_pandas = regional_distribution.toPandas()
seasonal_pandas = seasonal_pattern.toPandas()
treatment_pandas = treatment_response.toPandas()
total_samples = df.count()
result_data = {"total_samples": total_samples, "disease_distribution": disease_counts_pandas.to_dict('records'), "age_group_analysis": age_group_pandas.to_dict('records'), "severity_analysis": severity_pandas.to_dict('records'), "gender_analysis": gender_pandas.to_dict('records'), "family_history_analysis": family_pandas.to_dict('records'), "regional_analysis": regional_pandas.to_dict('records'), "seasonal_analysis": seasonal_pandas.to_dict('records'), "treatment_analysis": treatment_pandas.to_dict('records')}
return JsonResponse({"status": "success", "data": result_data})
def analyze_symptom_correlation_heatmap(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/disease_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "patient_symptoms").option("user", "root").option("password", "password").load()
symptom_columns = ["itching_intensity", "redness_level", "scaling_severity", "thickness_index", "inflammation_score", "pain_level", "burning_sensation", "dryness_index", "lesion_size", "distribution_area", "edge_clarity", "surface_texture", "color_intensity", "temperature_change", "sensitivity_level"]
correlation_matrix = []
for i, col1 in enumerate(symptom_columns):
correlation_row = []
for j, col2 in enumerate(symptom_columns):
if i <= j:
corr_value = df.select(corr(col1, col2).alias("correlation")).collect()[0]["correlation"]
correlation_row.append(float(corr_value) if corr_value is not None else 0.0)
else:
correlation_row.append(correlation_matrix[j][i])
correlation_matrix.append(correlation_row)
strong_correlations = []
for i in range(len(symptom_columns)):
for j in range(i+1, len(symptom_columns)):
corr_val = correlation_matrix[i][j]
if abs(corr_val) > 0.6:
strong_correlations.append({"symptom1": symptom_columns[i], "symptom2": symptom_columns[j], "correlation": corr_val, "strength": "强相关" if abs(corr_val) > 0.8 else "中等相关"})
disease_specific_correlations = {}
disease_types = df.select("disease_type").distinct().collect()
for disease_row in disease_types:
disease_type = disease_row["disease_type"]
disease_df = df.filter(col("disease_type") == disease_type)
disease_correlations = []
for i in range(len(symptom_columns)):
disease_corr_row = []
for j in range(len(symptom_columns)):
if i <= j:
corr_val = disease_df.select(corr(symptom_columns[i], symptom_columns[j]).alias("correlation")).collect()[0]["correlation"]
disease_corr_row.append(float(corr_val) if corr_val is not None else 0.0)
else:
disease_corr_row.append(disease_correlations[j][i] if j < len(disease_correlations) else 0.0)
disease_correlations.append(disease_corr_row)
disease_specific_correlations[disease_type] = disease_correlations
cluster_analysis = df.select(*symptom_columns).na.drop()
assembler = VectorAssembler(inputCols=symptom_columns, outputCol="features")
feature_df = assembler.transform(cluster_analysis)
kmeans = KMeans(k=3, seed=42, featuresCol="features", predictionCol="cluster")
model = kmeans.fit(feature_df)
clustered_df = model.transform(feature_df)
cluster_centers = model.clusterCenters()
cluster_symptom_profiles = []
for i, center in enumerate(cluster_centers):
profile = {"cluster_id": i, "symptom_profile": {symptom_columns[j]: float(center[j]) for j in range(len(symptom_columns))}}
cluster_symptom_profiles.append(profile)
result_data = {"correlation_matrix": {"symptoms": symptom_columns, "correlations": correlation_matrix}, "strong_correlations": strong_correlations, "disease_specific_correlations": disease_specific_correlations, "cluster_profiles": cluster_symptom_profiles}
return JsonResponse({"status": "success", "data": result_data})
def perform_patient_clustering_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/disease_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "patient_comprehensive_data").option("user", "root").option("password", "password").load()
feature_columns = ["age", "symptom_duration", "itching_intensity", "redness_level", "scaling_severity", "inflammation_score", "lesion_count", "affected_area_percentage", "family_history_score", "previous_treatment_count", "response_to_treatment", "comorbidity_count", "lifestyle_risk_score", "environmental_exposure_score", "stress_level"]
cleaned_df = df.select(*feature_columns, "patient_id", "disease_type", "gender").na.drop()
assembler = VectorAssembler(inputCols=feature_columns, outputCol="features")
feature_df = assembler.transform(cleaned_df)
optimal_k = 2
best_silhouette = -1
for k in range(2, 8):
kmeans = KMeans(k=k, seed=42, featuresCol="features", predictionCol="prediction")
model = kmeans.fit(feature_df)
predictions = model.transform(feature_df)
evaluator = ClusteringEvaluator(predictionCol="prediction", featuresCol="features", metricName="silhouette")
silhouette = evaluator.evaluate(predictions)
if silhouette > best_silhouette:
best_silhouette = silhouette
optimal_k = k
final_kmeans = KMeans(k=optimal_k, seed=42, featuresCol="features", predictionCol="cluster")
final_model = final_kmeans.fit(feature_df)
clustered_predictions = final_model.transform(feature_df)
cluster_statistics = clustered_predictions.groupBy("cluster").agg(count("*").alias("patient_count"), *[col(feature).alias(f"avg_{feature}") for feature in feature_columns[:5]])
cluster_disease_distribution = clustered_predictions.groupBy("cluster", "disease_type").agg(count("*").alias("count"))
cluster_gender_distribution = clustered_predictions.groupBy("cluster", "gender").agg(count("*").alias("count"))
cluster_centers = final_model.clusterCenters()
cluster_profiles = []
for i, center in enumerate(cluster_centers):
dominant_features = []
for j, feature_name in enumerate(feature_columns):
if center[j] > np.mean([center[k] for k in range(len(feature_columns))]):
dominant_features.append({"feature": feature_name, "value": float(center[j])})
cluster_profiles.append({"cluster_id": i, "center": [float(x) for x in center], "dominant_features": dominant_features[:5]})
risk_assessment = clustered_predictions.withColumn("risk_level", when(col("cluster") == 0, "低风险").when(col("cluster") == 1, "中等风险").when(col("cluster") == 2, "高风险").otherwise("极高风险"))
risk_distribution = risk_assessment.groupBy("risk_level").agg(count("*").alias("patient_count"), (count("*") * 100.0 / clustered_predictions.count()).alias("percentage"))
treatment_recommendation = {}
for i in range(optimal_k):
cluster_patients = clustered_predictions.filter(col("cluster") == i)
avg_response = cluster_patients.agg({"response_to_treatment": "avg"}).collect()[0][0]
avg_severity = cluster_patients.agg({"inflammation_score": "avg"}).collect()[0][0]
if avg_response > 7 and avg_severity < 5:
recommendation = "标准治疗方案"
elif avg_response < 5 or avg_severity > 7:
recommendation = "加强治疗方案"
else:
recommendation = "个性化治�疗方案"
treatment_recommendation[f"cluster_{i}"] = recommendation
cluster_stats_pandas = cluster_statistics.toPandas()
disease_dist_pandas = cluster_disease_distribution.toPandas()
gender_dist_pandas = cluster_gender_distribution.toPandas()
risk_dist_pandas = risk_distribution.toPandas()
result_data = {"optimal_clusters": optimal_k, "silhouette_score": best_silhouette, "cluster_statistics": cluster_stats_pandas.to_dict('records'), "cluster_profiles": cluster_profiles, "disease_distribution": disease_dist_pandas.to_dict('records'), "gender_distribution": gender_dist_pandas.to_dict('records'), "risk_assessment": risk_dist_pandas.to_dict('records'), "treatment_recommendations": treatment_recommendation}
return JsonResponse({"status": "success", "data": result_data})
基于大数据的红斑鳞状疾病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
