💖💖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
@TOC
基于大数据的乳腺癌诊断数据可视化分析系统介绍
《基于大数据的乳腺癌诊断数据可视化分析系统》是一个融合了先进大数据技术与医疗数据分析的综合性平台，该系统采用Hadoop分布式存储框架和Spark大数据处理引擎作为核心技术架构，支持Python和Java双语言开发模式，后端分别基于Django和Spring Boot框架构建，前端采用Vue+ElementUI+Echarts技术栈实现现代化的用户界面和丰富的数据可视化效果。系统通过HDFS分布式文件系统存储海量乳腺癌诊断数据，利用Spark SQL进行高效的数据查询和处理，结合Pandas和NumPy进行深度数据分析，实现了包括诊断类型统计分析、综合特征评分分析、凹度特征深度分析、诊断特征重要性分析、诊断特征均值比较分析、特征相关性热力图分析、几何特征聚类分析、多维度特征比较分析、特征异常值检测分析、标准误差稳定性分析、对称性与分形维数分析、肿瘤纹理特征分布分析、肿瘤形态特征分析、肿瘤尺寸特征分析以及最差特征值风险分析等十六个核心分析功能模块，同时配备完整的用户管理体系、个人信息管理、密码修改等基础功能，并提供直观的大屏可视化展示界面，通过MySQL数据库保障数据的持久化存储，整个系统不仅展现了大数据技术在医疗诊断领域的应用价值，更为乳腺癌的早期筛查和精准诊断提供了强有力的数据支撑和决策依据。
基于大数据的乳腺癌诊断数据可视化分析系统演示视频
基于大数据的乳腺癌诊断数据可视化分析系统演示图片
基于大数据的乳腺癌诊断数据可视化分析系统代码展示
spark = SparkSession.builder.appName("BreastCancerAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def analyze_feature_importance(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/breast_cancer").option("dbtable", "diagnosis_data").option("user", "root").option("password", "123456").load()
feature_columns = ['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave_points_mean', 'symmetry_mean', 'fractal_dimension_mean']
diagnosis_column = 'diagnosis'
feature_data = df.select(*feature_columns, diagnosis_column)
feature_data = feature_data.na.drop()
diagnosis_encoded = feature_data.withColumn("diagnosis_numeric", when(col("diagnosis") == "M", 1).otherwise(0))
correlation_results = []
for feature in feature_columns:
correlation = diagnosis_encoded.stat.corr(feature, "diagnosis_numeric")
importance_score = abs(correlation)
correlation_results.append({'feature': feature, 'importance': importance_score, 'correlation': correlation})
correlation_results.sort(key=lambda x: x['importance'], reverse=True)
top_features = correlation_results[:8]
variance_data = []
for feature in feature_columns:
feature_stats = diagnosis_encoded.agg(variance(col(feature)).alias("variance")).collect()[0]
variance_score = feature_stats["variance"]
variance_data.append({'feature': feature, 'variance': variance_score})
variance_data.sort(key=lambda x: x['variance'], reverse=True)
final_importance = []
for i, item in enumerate(correlation_results):
variance_rank = next((j for j, v in enumerate(variance_data) if v['feature'] == item['feature']), 0)
combined_score = (item['importance'] * 0.7) + ((len(variance_data) - variance_rank) / len(variance_data) * 0.3)
final_importance.append({'feature': item['feature'], 'importance_score': combined_score, 'correlation': item['correlation']})
final_importance.sort(key=lambda x: x['importance_score'], reverse=True)
result_data = {'status': 'success', 'feature_importance': final_importance, 'top_features': [item['feature'] for item in final_importance[:5]]}
return JsonResponse(result_data)
def generate_correlation_heatmap(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/breast_cancer").option("dbtable", "diagnosis_data").option("user", "root").option("password", "123456").load()
numeric_features = ['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave_points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se']
feature_data = df.select(*numeric_features)
feature_data = feature_data.na.drop()
correlation_matrix = []
for i, feature1 in enumerate(numeric_features):
correlation_row = []
for j, feature2 in enumerate(numeric_features):
if i == j:
correlation_value = 1.0
else:
correlation_value = feature_data.stat.corr(feature1, feature2)
correlation_row.append(round(correlation_value, 4))
correlation_matrix.append(correlation_row)
strong_correlations = []
for i in range(len(numeric_features)):
for j in range(i+1, len(numeric_features)):
corr_value = correlation_matrix[i][j]
if abs(corr_value) > 0.7:
strong_correlations.append({'feature1': numeric_features[i], 'feature2': numeric_features[j], 'correlation': corr_value, 'strength': 'strong' if abs(corr_value) > 0.8 else 'moderate'})
heatmap_data = []
for i, row in enumerate(correlation_matrix):
for j, value in enumerate(row):
heatmap_data.append({'x': j, 'y': i, 'value': value, 'feature_x': numeric_features[j], 'feature_y': numeric_features[i]})
result_data = {'status': 'success', 'correlation_matrix': correlation_matrix, 'feature_names': numeric_features, 'heatmap_data': heatmap_data, 'strong_correlations': strong_correlations}
return JsonResponse(result_data)
def perform_geometric_clustering(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/breast_cancer").option("dbtable", "diagnosis_data").option("user", "root").option("password", "123456").load()
geometric_features = ['radius_mean', 'perimeter_mean', 'area_mean', 'radius_se', 'perimeter_se', 'area_se', 'radius_worst', 'perimeter_worst', 'area_worst']
feature_data = df.select(*geometric_features, 'diagnosis', 'id')
feature_data = feature_data.na.drop()
assembler = VectorAssembler(inputCols=geometric_features, outputCol="features")
feature_vector = assembler.transform(feature_data)
scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures", withStd=True, withMean=True)
scaler_model = scaler.fit(feature_vector)
scaled_data = scaler_model.transform(feature_vector)
kmeans = KMeans(k=3, featuresCol="scaledFeatures", predictionCol="cluster", seed=42, maxIter=100)
kmeans_model = kmeans.fit(scaled_data)
clustered_data = kmeans_model.transform(scaled_data)
cluster_centers = kmeans_model.clusterCenters()
cluster_summary = clustered_data.groupBy("cluster").agg(count("*").alias("count"), avg("radius_mean").alias("avg_radius"), avg("area_mean").alias("avg_area"), avg("perimeter_mean").alias("avg_perimeter")).collect()
diagnosis_cluster_analysis = clustered_data.groupBy("cluster", "diagnosis").agg(count("*").alias("count")).collect()
cluster_results = []
for row in cluster_summary:
cluster_id = row["cluster"]
cluster_info = {'cluster_id': cluster_id, 'sample_count': row["count"], 'avg_radius':
round(row["avg_radius"], 3), 'avg_area': round(row["avg_area"], 3), 'avg_perimeter': round(row["avg_perimeter"], 3)}
diagnosis_dist = [item for item in diagnosis_cluster_analysis if item["cluster"] == cluster_id]
malignant_count = next((item["count"] for item in diagnosis_dist if item["diagnosis"] == "M"), 0)
benign_count = next((item["count"] for item in diagnosis_dist if item["diagnosis"] == "B"), 0)
cluster_info['malignant_ratio'] = round(malignant_count / (malignant_count + benign_count), 3) if (malignant_count + benign_count) > 0 else 0
cluster_results.append(cluster_info)
sample_data = clustered_data.select("id", "cluster", "radius_mean", "area_mean", "perimeter_mean", "diagnosis").limit(100).collect()
cluster_samples = [{'id': row["id"], 'cluster': row["cluster"], 'radius': round(row["radius_mean"], 3), 'area': round(row["area_mean"], 3), 'perimeter': round(row["perimeter_mean"], 3), 'diagnosis': row["diagnosis"]} for row in sample_data]
result_data = {'status': 'success', 'cluster_results': cluster_results, 'cluster_samples': cluster_samples, 'total_clusters': 3}
return JsonResponse(result_data)
基于大数据的乳腺癌诊断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
