💖💖作者：计算机毕业设计江挽 💙💙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
基于大数据的医学生健康程度数据分析系统介绍
医学生健康程度数据分析系统是一套基于Hadoop+Spark大数据技术架构的综合性健康数据处理平台，专门针对医学生群体的身心健康状况进行深度数据挖掘和智能分析。系统采用Python作为核心开发语言，后端框架选用Django进行业务逻辑处理，前端采用Vue+ElementUI+Echarts技术栈构建用户交互界面，通过MySQL数据库进行数据持久化存储。系统核心功能涵盖医学生健康程度数据管理、倦怠共情能力分析、人口学特征分析、重点群体画像分析、心理健康评估分析以及学业健康关联分析等多个维度。通过Spark SQL进行大规模数据处理和统计分析，结合Pandas、NumPy等数据科学库进行数据清洗和特征工程，最终通过Echarts图表组件将分析结果以可视化形式展现给用户。系统能够有效处理大量医学生健康相关数据，为教育管理部门和医学院校提供科学的数据支撑，帮助识别医学生群体中的健康风险因素，为制定针对性的健康干预措施提供参考依据。
基于大数据的医学生健康程度数据分析系统演示视频
基于大数据的医学生健康程度数据分析系统演示图片
基于大数据的医学生健康程度数据分析系统代码展示
spark = SparkSession.builder.appName("MedicalStudentHealthAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def analyze_burnout_empathy(self, data_df):
burnout_scores = data_df.select("student_id", "emotional_exhaustion", "depersonalization", "personal_accomplishment").rdd.map(lambda row: (row.student_id, (row.emotional_exhaustion * 0.4 + row.depersonalization * 0.3 + (100 - row.personal_accomplishment) * 0.3)))
empathy_scores = data_df.select("student_id", "perspective_taking", "fantasy", "empathic_concern", "personal_distress").rdd.map(lambda row: (row.student_id, (row.perspective_taking * 0.3 + row.fantasy * 0.2 + row.empathic_concern * 0.3 + (100 - row.personal_distress) * 0.2)))
combined_analysis = burnout_scores.join(empathy_scores).map(lambda x: (x[0], x[1][0], x[1][1], x[1][0] / x[1][1] if x[1][1] != 0 else 0))
risk_levels = combined_analysis.map(lambda x: (x[0], x[1], x[2], x[3], "high_risk" if x[1] > 70 and x[2] < 60 else "moderate_risk" if x[1] > 50 else "low_risk"))
correlation_coefficient = combined_analysis.map(lambda x: (x[1], x[2])).collect()
burnout_values = [item[0] for item in correlation_coefficient]
empathy_values = [item[1] for item in correlation_coefficient]
correlation = np.corrcoef(burnout_values, empathy_values)[0, 1] if len(burnout_values) > 1 else 0
result_summary = risk_levels.map(lambda x: (x[4], 1)).reduceByKey(lambda a, b: a + b).collect()
avg_burnout = combined_analysis.map(lambda x: x[1]).mean()
avg_empathy = combined_analysis.map(lambda x: x[2]).mean()
analysis_result = {"correlation": correlation, "avg_burnout": avg_burnout, "avg_empathy": avg_empathy, "risk_distribution": dict(result_summary), "detailed_scores": risk_levels.collect()}
trend_analysis = combined_analysis.filter(lambda x: x[1] > avg_burnout and x[2] < avg_empathy).count()
intervention_suggestions = []
if correlation < -0.3:
intervention_suggestions.append("建议加强共情能力培训以降低倦怠感")
if trend_analysis > len(combined_analysis.collect()) * 0.3:
intervention_suggestions.append("需要重点关注高倦怠低共情群体")
analysis_result["intervention_suggestions"] = intervention_suggestions
return analysis_result
def evaluate_psychological_health(self, data_df):
anxiety_scores = data_df.select("student_id", "anxiety_level", "sleep_quality", "stress_frequency").rdd.map(lambda row: (row.student_id, row.anxiety_level * 0.5 + (100 - row.sleep_quality) * 0.3 + row.stress_frequency * 0.2))
depression_indicators = data_df.select("student_id", "mood_rating", "energy_level", "concentration_ability").rdd.map(lambda row: (row.student_id, (100 - row.mood_rating) * 0.4 + (100 - row.energy_level) * 0.3 + (100 - row.concentration_ability) * 0.3))
social_support = data_df.select("student_id", "family_support", "peer_support", "mentor_support").rdd.map(lambda row: (row.student_id, (row.family_support * 0.4 + row.peer_support * 0.3 + row.mentor_support * 0.3)))
comprehensive_scores = anxiety_scores.join(depression_indicators).join(social_support).map(lambda x: (x[0], x[1][0][0], x[1][0][1], x[1][1], (x[1][0][0] + x[1][0][1]) / 2 - x[1][1] * 0.3))
mental_health_categories = comprehensive_scores.map(lambda x: (x[0], x[4], "excellent" if x[4] < 20 else "good" if x[4] < 40 else "fair" if x[4] < 60 else "poor"))
risk_assessment = mental_health_categories.filter(lambda x: x[2] in ["fair", "poor"]).map(lambda x: (x[0], x[1], x[2], "immediate_attention" if x[1] > 70 else "monitoring_required"))
statistical_summary = mental_health_categories.map(lambda x: (x[2], 1)).reduceByKey(lambda a, b: a + b).collect()
high_risk_students = risk_assessment.filter(lambda x: x[3] == "immediate_attention").collect()
correlation_matrix = comprehensive_scores.map(lambda x: (x[1], x[2], x[3])).collect()
avg_anxiety = sum([item[0] for item in correlation_matrix]) / len(correlation_matrix) if correlation_matrix else 0
avg_depression = sum([item[1] for item in correlation_matrix]) / len(correlation_matrix) if correlation_matrix else 0
avg_social_support = sum([item[2] for item in correlation_matrix]) / len(correlation_matrix) if correlation_matrix else 0
protective_factors = comprehensive_scores.filter(lambda x: x[3] > 70 and x[4] < 30).count()
vulnerability_factors = comprehensive_scores.filter(lambda x: x[1] > 60 or x[2] > 60).count()
evaluation_result = {"health_distribution": dict(statistical_summary), "high_risk_count": len(high_risk_students), "avg_scores": {"anxiety": avg_anxiety, "depression": avg_depression, "social_support": avg_social_support}, "protective_factors": protective_factors, "vulnerability_factors": vulnerability_factors, "detailed_assessments": mental_health_categories.collect()}
return evaluation_result
def analyze_target_group_profile(self, data_df):
demographic_features = data_df.select("student_id", "age", "gender", "grade_level", "academic_performance", "family_background").rdd.map(lambda row: (row.student_id, {"age": row.age, "gender": row.gender, "grade": row.grade_level, "performance": row.academic_performance, "background": row.family_background}))
health_indicators = data_df.select("student_id", "bmi", "exercise_frequency", "sleep_hours", "nutrition_score").rdd.map(lambda row: (row.student_id, {"bmi": row.bmi, "exercise": row.exercise_frequency, "sleep": row.sleep_hours, "nutrition": row.nutrition_score}))
behavioral_patterns = data_df.select("student_id", "study_hours", "social_activity", "screen_time", "substance_use").rdd.map(lambda row: (row.student_id, {"study_time": row.study_hours, "social": row.social_activity, "screen": row.screen_time, "substance": row.substance_use}))
integrated_profiles = demographic_features.join(health_indicators).join(behavioral_patterns).map(lambda x: (x[0], {**x[1][0][0], **x[1][0][1], **x[1][1]}))
risk_scoring = integrated_profiles.map(lambda x: (x[0], x[1], calculate_risk_score(x[1]["bmi"], x[1]["exercise"], x[1]["sleep"], x[1]["performance"], x[1]["substance"])))
def calculate_risk_score(bmi, exercise, sleep, performance, substance):
risk_score = 0
risk_score += 20 if bmi > 28 or bmi < 18.5 else 0
risk_score += 15 if exercise < 2 else 0
risk_score += 25 if sleep < 6 or sleep > 10 else 0
risk_score += 20 if performance < 60 else 0
risk_score += 30 if substance > 0 else 0
return min(risk_score, 100)
high_risk_groups = risk_scoring.filter(lambda x: x[2] > 60).map(lambda x: (x[0], x[1], x[2], "high_priority"))
moderate_risk_groups = risk_scoring.filter(lambda x: 30 <= x[2] <= 60).map(lambda x: (x[0], x[1], x[2], "moderate_priority"))
cluster_analysis = risk_scoring.map(lambda x: (x[1]["gender"], x[1]["grade"], x[2])).groupByKey().mapValues(lambda values: {"count": len(list(values)), "avg_risk": sum(list(values)) / len(list(values))})
gender_risk_distribution = risk_scoring.map(lambda x: (x[1]["gender"], x[2])).groupByKey().mapValues(lambda risks: {"total": len(list(risks)), "high_risk": len([r for r in risks if r > 60])})
grade_risk_distribution = risk_scoring.map(lambda x: (x[1]["grade"], x[2])).groupByKey().mapValues(lambda risks: {"total": len(list(risks)), "avg_risk": sum(list(risks)) / len(list(risks))})
intervention_priorities = high_risk_groups.map(lambda x: (x[1]["grade"], x[1]["gender"], 1)).reduceByKey(lambda a, b: a + b).collect()
profile_summary = {"high_risk_students": high_risk_groups.count(), "moderate_risk_students": moderate_risk_groups.count(), "gender_distribution": gender_risk_distribution.collectAsMap(), "grade_distribution": grade_risk_distribution.collectAsMap(), "intervention_priorities": intervention_priorities, "detailed_profiles": risk_scoring.collect()}
return profile_summary
基于大数据的医学生健康程度数据分析系统文档展示
💖💖作者：计算机毕业设计江挽 💙💙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
