前言

????作者：計(jì)算機(jī)程序員小楊
????個(gè)人簡(jiǎn)介：我是一名計(jì)算機(jī)相關(guān)專業(yè)的從業(yè)者，擅長(zhǎng)Java、微信小程序、Python、Golang、安卓Android等多個(gè)IT方向。會(huì)做一些項(xiàng)目定制化開(kāi)發(fā)、代碼講解、答辯教學(xué)、文檔編寫(xiě)、也懂一些降重方面的技巧。熱愛(ài)技術(shù)，喜歡鉆研新工具和框架，也樂(lè)于通過(guò)代碼解決實(shí)際問(wèn)題，大家有技術(shù)代碼這一塊的問(wèn)題可以問(wèn)我！
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????文末獲取源碼聯(lián)系 計(jì)算機(jī)程序員小楊
????
網(wǎng)站實(shí)戰(zhàn)項(xiàng)目
安卓/小程序?qū)崙?zhàn)項(xiàng)目
大數(shù)據(jù)實(shí)戰(zhàn)項(xiàng)目
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計(jì)算機(jī)畢業(yè)設(shè)計(jì)選題
????

一.開(kāi)發(fā)工具簡(jiǎn)介

大數(shù)據(jù)框架：Hadoop+Spark（本次沒(méi)用Hive，支持定制）
開(kāi)發(fā)語(yǔ)言：Python+Java（兩個(gè)版本都支持）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（兩個(gè)版本都支持）
前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
詳細(xì)技術(shù)點(diǎn)：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
數(shù)據(jù)庫(kù)：MySQL

二.系統(tǒng)內(nèi)容簡(jiǎn)介

基于大數(shù)據(jù)的分化型甲狀腺癌復(fù)發(fā)數(shù)據(jù)可視化分析系統(tǒng)是一套運(yùn)用先進(jìn)大數(shù)據(jù)技術(shù)構(gòu)建的醫(yī)療數(shù)據(jù)智能分析平臺(tái)，該系統(tǒng)采用Hadoop分布式存儲(chǔ)框架和Spark大數(shù)據(jù)處理引擎作為核心技術(shù)架構(gòu)，通過(guò)Python語(yǔ)言結(jié)合Django后端框架實(shí)現(xiàn)數(shù)據(jù)處理邏輯，前端采用Vue+ElementUI+Echarts技術(shù)棧打造直觀友好的數(shù)據(jù)可視化界面。系統(tǒng)圍繞分化型甲狀腺癌患者的復(fù)發(fā)風(fēng)險(xiǎn)評(píng)估需求，構(gòu)建了包含系統(tǒng)首頁(yè)、用戶中心、用戶管理、甲狀腺數(shù)據(jù)管理在內(nèi)的完整功能模塊，重點(diǎn)實(shí)現(xiàn)了患者人口特征分析、多維因素關(guān)聯(lián)分析、臨床病理特征分析、甲狀腺功能指標(biāo)分析以及患者治療效果分析等核心功能。通過(guò)運(yùn)用Spark SQL進(jìn)行大規(guī)模數(shù)據(jù)查詢處理，結(jié)合Pandas和NumPy進(jìn)行數(shù)據(jù)科學(xué)計(jì)算，系統(tǒng)能夠?qū)Ａ考谞钕侔┗颊邤?shù)據(jù)進(jìn)行深度挖掘和統(tǒng)計(jì)分析，為醫(yī)療工作者提供患者復(fù)發(fā)風(fēng)險(xiǎn)預(yù)測(cè)、治療效果評(píng)估和臨床決策支持，同時(shí)通過(guò)豐富的圖表和可視化組件將復(fù)雜的醫(yī)療數(shù)據(jù)以直觀易懂的方式呈現(xiàn)，實(shí)現(xiàn)了大數(shù)據(jù)技術(shù)在精準(zhǔn)醫(yī)療領(lǐng)域的創(chuàng)新應(yīng)用。

三.系統(tǒng)功能演示

看完這個(gè)基于Hadoop的甲狀腺癌數(shù)據(jù)系統(tǒng)，你可能會(huì)重新規(guī)劃自己的畢設(shè)方向

四.系統(tǒng)界面展示

大屏幕.png

多維因素關(guān)聯(lián)分析.png

患者人口特征分析.png

患者治療效果分析.png

甲狀腺功能指標(biāo)分析.png

甲狀腺數(shù)據(jù)管理.png

臨床病理特征分析.png

用戶管理.png

五.系統(tǒng)源碼展示

# 核心功能1：患者人口特征分析
def analyze_population_characteristics(request):
    """分析患者人口特征分布"""
    # 獲取所有甲狀腺癌患者數(shù)據(jù)
    patients = ThyroidPatient.objects.all()
    
    # 按年齡段分組統(tǒng)計(jì)
    age_groups = {'20-30': 0, '31-40': 0, '41-50': 0, '51-60': 0, '60+': 0}
    for patient in patients:
        age = patient.age
        if 20 <= age <= 30:
            age_groups['20-30'] += 1
        elif 31 <= age <= 40:
            age_groups['31-40'] += 1
        elif 41 <= age <= 50:
            age_groups['41-50'] += 1
        elif 51 <= age <= 60:
            age_groups['51-60'] += 1
        else:
            age_groups['60+'] += 1
    
    # 性別分布統(tǒng)計(jì)
    gender_stats = patients.values('gender').annotate(count=Count('id'))
    male_count = sum(item['count'] for item in gender_stats if item['gender'] == 'M')
    female_count = sum(item['count'] for item in gender_stats if item['gender'] == 'F')
    
    # 地區(qū)分布分析
    region_stats = patients.values('region').annotate(count=Count('id')).order_by('-count')[:10]
    
    # 計(jì)算復(fù)發(fā)率按年齡段分布
    recurrence_by_age = {}
    for age_range in age_groups.keys():
        total_in_range = age_groups[age_range]
        if age_range == '20-30':
            recurred = patients.filter(age__gte=20, age__lte=30, is_recurrence=True).count()
        elif age_range == '31-40':
            recurred = patients.filter(age__gte=31, age__lte=40, is_recurrence=True).count()
        elif age_range == '41-50':
            recurred = patients.filter(age__gte=41, age__lte=50, is_recurrence=True).count()
        elif age_range == '51-60':
            recurred = patients.filter(age__gte=51, age__lte=60, is_recurrence=True).count()
        else:
            recurred = patients.filter(age__gt=60, is_recurrence=True).count()
        
        recurrence_rate = (recurred / total_in_range * 100) if total_in_range > 0 else 0
        recurrence_by_age[age_range] = round(recurrence_rate, 2)
    
    # 構(gòu)建返回?cái)?shù)據(jù)
    result_data = {
        'age_distribution': age_groups,
        'gender_distribution': {'male': male_count, 'female': female_count},
        'region_top10': list(region_stats),
        'recurrence_by_age': recurrence_by_age,
        'total_patients': patients.count()
    }
    
    return JsonResponse({'status': 'success', 'data': result_data})

# 核心功能2：多維因素關(guān)聯(lián)分析
def multi_dimensional_correlation_analysis(request):
    """多維因素關(guān)聯(lián)性分析"""
    # 獲取所有患者完整數(shù)據(jù)
    patients_data = ThyroidPatient.objects.select_related().all()
    
    # 構(gòu)建分析矩陣
    correlation_matrix = {}
    factors = ['age', 'tumor_size', 'tsh_level', 't3_level', 't4_level', 'thyroglobulin']
    
    # 計(jì)算各因素與復(fù)發(fā)的相關(guān)性
    for factor in factors:
        factor_values = []
        recurrence_values = []
        
        for patient in patients_data:
            factor_value = getattr(patient, factor, None)
            if factor_value is not None:
                factor_values.append(float(factor_value))
                recurrence_values.append(1 if patient.is_recurrence else 0)
        
        # 計(jì)算皮爾遜相關(guān)系數(shù)
        if len(factor_values) > 1:
            correlation_coefficient = np.corrcoef(factor_values, recurrence_values)[0, 1]
            correlation_matrix[factor] = round(correlation_coefficient, 4)
    
    # 分析不同腫瘤大小組的復(fù)發(fā)情況
    tumor_size_analysis = {}
    size_ranges = {'<1cm': (0, 10), '1-2cm': (10, 20), '2-4cm': (20, 40), '>4cm': (40, 100)}
    
    for size_label, (min_size, max_size) in size_ranges.items():
        patients_in_range = patients_data.filter(
            tumor_size__gte=min_size, 
            tumor_size__lt=max_size
        )
        total_count = patients_in_range.count()
        recurrence_count = patients_in_range.filter(is_recurrence=True).count()
        
        recurrence_rate = (recurrence_count / total_count * 100) if total_count > 0 else 0
        tumor_size_analysis[size_label] = {
            'total': total_count,
            'recurred': recurrence_count,
            'rate': round(recurrence_rate, 2)
        }
    
    # TSH水平分組分析
    tsh_groups = {'正常': (0.4, 4.0), '偏低': (0, 0.4), '偏高': (4.0, 100)}
    tsh_analysis = {}
    
    for tsh_label, (min_tsh, max_tsh) in tsh_groups.items():
        patients_in_group = patients_data.filter(
            tsh_level__gte=min_tsh,
            tsh_level__lt=max_tsh
        )
        group_total = patients_in_group.count()
        group_recurred = patients_in_group.filter(is_recurrence=True).count()
        
        group_rate = (group_recurred / group_total * 100) if group_total > 0 else 0
        tsh_analysis[tsh_label] = {
            'count': group_total,
            'recurrence_rate': round(group_rate, 2)
        }
    
    # 年齡與腫瘤大小交叉分析
    age_tumor_cross = {}
    for patient in patients_data:
        age_group = '青年' if patient.age < 40 else ('中年' if patient.age < 60 else '老年')
        tumor_group = '小' if patient.tumor_size < 20 else ('中' if patient.tumor_size < 40 else '大')
        
        key = f"{age_group}_{tumor_group}"
        if key not in age_tumor_cross:
            age_tumor_cross[key] = {'total': 0, 'recurred': 0}
        
        age_tumor_cross[key]['total'] += 1
        if patient.is_recurrence:
            age_tumor_cross[key]['recurred'] += 1
    
    # 計(jì)算交叉分析復(fù)發(fā)率
    for key in age_tumor_cross:
        total = age_tumor_cross[key]['total']
        recurred = age_tumor_cross[key]['recurred']
        age_tumor_cross[key]['rate'] = round((recurred / total * 100), 2) if total > 0 else 0
    
    analysis_result = {
        'correlation_matrix': correlation_matrix,
        'tumor_size_analysis': tumor_size_analysis,
        'tsh_level_analysis': tsh_analysis,
        'age_tumor_cross_analysis': age_tumor_cross
    }
    
    return JsonResponse({'status': 'success', 'data': analysis_result})

# 核心功能3：患者治療效果分析
def treatment_effectiveness_analysis(request):
    """患者治療效果綜合分析"""
    # 獲取所有治療記錄數(shù)據(jù)
    treatment_records = TreatmentRecord.objects.select_related('patient').all()
    
    # 按治療方式分組統(tǒng)計(jì)效果
    treatment_methods = ['手術(shù)', '碘131治療', 'TSH抑制治療', '放療', '化療']
    treatment_effectiveness = {}
    
    for method in treatment_methods:
        method_records = treatment_records.filter(treatment_method=method)
        total_cases = method_records.count()
        
        # 統(tǒng)計(jì)不同效果等級(jí)
        excellent = method_records.filter(treatment_effect='優(yōu)').count()
        good = method_records.filter(treatment_effect='良').count()
        fair = method_records.filter(treatment_effect='可').count()
        poor = method_records.filter(treatment_effect='差').count()
        
        # 計(jì)算有效率（優(yōu)+良）
        effective_rate = ((excellent + good) / total_cases * 100) if total_cases > 0 else 0
        
        # 分析該治療方式的復(fù)發(fā)情況
        patients_with_method = [record.patient for record in method_records]
        recurrence_count = sum(1 for patient in patients_with_method if patient.is_recurrence)
        recurrence_rate = (recurrence_count / len(patients_with_method) * 100) if patients_with_method else 0
        
        treatment_effectiveness[method] = {
            'total_cases': total_cases,
            'excellent': excellent,
            'good': good,
            'fair': fair,
            'poor': poor,
            'effective_rate': round(effective_rate, 2),
            'recurrence_rate': round(recurrence_rate, 2)
        }
    
    # 分析治療周期與效果關(guān)系
    cycle_analysis = {}
    cycle_ranges = ['1-3個(gè)月', '4-6個(gè)月', '7-12個(gè)月', '1年以上']
    
    for cycle_range in cycle_ranges:
        if cycle_range == '1-3個(gè)月':
            records = treatment_records.filter(treatment_duration__lte=90)
        elif cycle_range == '4-6個(gè)月':
            records = treatment_records.filter(treatment_duration__gt=90, treatment_duration__lte=180)
        elif cycle_range == '7-12個(gè)月':
            records = treatment_records.filter(treatment_duration__gt=180, treatment_duration__lte=365)
        else:
            records = treatment_records.filter(treatment_duration__gt=365)
        
        cycle_total = records.count()
        cycle_effective = records.filter(treatment_effect__in=['優(yōu)', '良']).count()
        cycle_effective_rate = (cycle_effective / cycle_total * 100) if cycle_total > 0 else 0
        
        cycle_analysis[cycle_range] = {
            'cases': cycle_total,
            'effective_cases': cycle_effective,
            'effectiveness': round(cycle_effective_rate, 2)
        }
    
    # 分析并發(fā)癥發(fā)生率
    complication_stats = {}
    all_complications = treatment_records.values_list('complications', flat=True)
    complication_types = ['聲帶麻痹', '低鈣血癥', '出血', '感染', '無(wú)']
    
    for comp_type in complication_types:
        comp_count = sum(1 for comp in all_complications if comp_type in str(comp))
        comp_rate = (comp_count / len(all_complications) * 100) if all_complications else 0
        complication_stats[comp_type] = {
            'count': comp_count,
            'rate': round(comp_rate, 2)
        }
    
    # 生存期分析（按治療效果分組）
    survival_analysis = {}
    effect_levels = ['優(yōu)', '良', '可', '差']
    
    for level in effect_levels:
        level_records = treatment_records.filter(treatment_effect=level)
        survival_periods = []
        
        for record in level_records:
            if record.follow_up_time:
                survival_periods.append(record.follow_up_time)
        
        if survival_periods:
            avg_survival = sum(survival_periods) / len(survival_periods)
            max_survival = max(survival_periods)
            min_survival = min(survival_periods)
            
            survival_analysis[level] = {
                'case_count': len(survival_periods),
                'average_survival': round(avg_survival, 1),
                'max_survival': max_survival,
                'min_survival': min_survival
            }
    
    # 治療費(fèi)用效益分析
    cost_benefit_analysis = {}
    for method in treatment_methods:
        method_records = treatment_records.filter(treatment_method=method)
        if method_records.exists():
            avg_cost = method_records.aggregate(Avg('treatment_cost'))['treatment_cost__avg']
            effective_cases = method_records.filter(treatment_effect__in=['優(yōu)', '良']).count()
            total_cases = method_records.count()
            
            cost_per_effective_case = (avg_cost * total_cases / effective_cases) if effective_cases > 0 else 0
            
            cost_benefit_analysis[method] = {
                'average_cost': round(avg_cost, 2) if avg_cost else 0,
                'cost_effectiveness_ratio': round(cost_per_effective_case, 2)
            }
    
    treatment_analysis_result = {
        'treatment_effectiveness': treatment_effectiveness,
        'cyc   
    return JsonResponse({'st      'complication_statistics': complication_stats,
        'survival_analysis': survival_analysis,
        'cost_benefit_analysis': cost_benefit_analysis
    }
    
    return JsonResponse({'status': 'success', 'data': treatment_analysis_result})

六.系統(tǒng)文檔展示

結(jié)束

????
網(wǎng)站實(shí)戰(zhàn)項(xiàng)目
安卓/小程序?qū)崙?zhàn)項(xiàng)目
大數(shù)據(jù)實(shí)戰(zhàn)項(xiàng)目
深度學(xué)習(xí)實(shí)戰(zhàn)項(xiàng)目
計(jì)算機(jī)畢業(yè)設(shè)計(jì)選題
????

????文末獲取源碼聯(lián)系 計(jì)算機(jī)程序員小楊