Adopting Random Forest for Predicting the Risk of Cerebrovas

发布时间：2025-01-10 22:48

　　通过一个具有代表性的人群样本,描述大脑动脉的复杂结构,对于诊断、分析和预测病理状态具有重要意义。磁共振血管造影可显示脑动脉血管。随着自动追踪和重建技术的出现,神经元三维重建数据的数量激增,神经形态学研究也随之兴起。然而,缺乏机器驱动的注释模式来自动检测和预测受试者可能患脑血管疾病的障碍或风险,使用神经形态学测量值仍然是这门学科的一个障碍。随机森林(RF)是一种常用的机器学习方法,在生物科学、经济学、化学工程、农业科学、医学研究等多个领域,在病理状态的诊断和预测等关键应用中都取得了竞争性的成功。它是一种由一个简单的树预测器组成的技术,当一组预测器值作为输入时,每棵树都会产生一个响应。本文对机器学习领域进行了全面的研究,采用基于决策树的随机算法randomforest对来自BraVa数据集的44名受试者的脑血管疾病的可能性进行了预测。在脑血管疾病的分析和预测方面,本文利用SPSS统计工具,实现了被诊断为脑血管疾病的各指标之间的独立性和相关性。计算了描述整个血管结构的各种定标器参数在总体尺寸、分支特征、分叉角和对称性方面的汇总统计。本研究所用数据的总体尺寸变异性与人体尺寸其他参数的报告值相似。...

【文章页数】：79 页

【学位级别】：硕士

【文章目录】：
摘要
ABSTRACT
1 INTRODUCTION
    1.1 BACKGROUND OF STUDY
    1.2 SIGNIFICANCE OF RF FOR MORPHOMETRIC ANALYSIS AND PREDICTION
    1.3 PURPOSE OF RANDOM FOREST FOR AUTOMATIC DIAGNOSES
    1.4 OBJECTIVES OF THE RF ALGORITHM FOR NEUROMORPHOLOGICAL ANALYSIS
2 LITERATURE REVIEW OF RF IN NEUROSCIENCE
    2.1 NEUROSCIENCE
    2.2 NEURONS
        2.2.1 NEUROMORPHOLOGY
        2.2.2 NEURON SIZE AND SHAPE
        2.2.3 DEVELOPMENT IN CELL GROWTH
        2.2.4 MORPHOLOGICAL PLASTICITY
        2.2.5 EXTRINSIC VS. INTRINSIC INFLUENCES
    2.3 NEURON MINING PIPELINE
        2.3.1 NEURON DATA ACQUISITION
        2.3.2 NEURON FEATURE EXTRACTION
        2.3.3 PROCESSING DATA FROM NEUROMORPHOLOGICAL FEATURES
        2.3.4 NORMALIZATION
        2.3.5 MISSING VALUE TREATMENT
        2.3.6 DATA UNIFICATION AND CONSOLIDATION
        2.3.7 ADDRESS IMBALANCE DATASET
        2.3.8 EXCLUSION OF CONFOUNDING VARIABLES
        2.3.9 DIMENSIONALITY REDUCTION
        2.3.10 FEATURE SELECTION
        2.3.11 UNSUPERVISED LEARNING
        2.3.12 SUPERVISED LEARNING
        2.3.13 MULTILABEL AND MULTICLASS CLASSIFICATION
    2.4 RANDOM FOREST AS A TOOL FOR AUTOMATIC PREDICTION
        2.4.1 TYPES OF DECISION TREES
        2.4.2 RANDOM FOREST AS METRIC FOR PREDICTION
        2.4.3 RANDOM FOREST ANALYSIS ON DIABETES COMPLICATION DATA
        2.4.4 RF ENSEMBLES FOR DETECTION AND PREDICTION OF ALZHEIMER'S DISEASE WITH A GOOD BETWEEN-COHORT ROBUSTNESS
        2.4.5 RANDOM FOREST ATTRIBUTION SELECTION MEASURES
3 RANDOM FOREST AND STATISTICAL METHODOLOGY
    3.1 SUBJECT AND BRAVA DATA ACQUISITION
        3.1.1 DIGITAL RECONSTRUCTION
        3.1.2 MORPHOLOGICAL ANALYSIS
    3.2 EXPERIMENTAL SETTINGS AND RESULTS FROM THE BRAVA DATASET AND DIABETES DATASET
        3.2.1 THE DATASETS EMPLOYED
        3.2.2 EXPERIMENT 3A
        3.2.3 EXPERIMENT 3B AND 3C
4 RANDOM FOREST AND SPSS RESULTS AND ANALYSIS
    4.1 QUANTITATIVE ANATOMY OF CEREBRAL ARTERIES
    4.2 RANDOM FOREST FOR THE BRAVA DATASET
        4.2.1 TESTING RANDOM FOREST WITH BRAVA (AGE/CONTRACTION)
        4.2.2 TESTING RANDOM FOREST ALGORITHM WITH THE DIABETES DATASET
5 CONCLUSIONS
    5.1 RF ALGORITHM CONCLUSION
    5.2 RECOMMENDATION
ACKNOWLEDGEMENT
REFERENCES
APPENDIX OF CORRELATION BETWEEN THE VARIOUS ARTERIAL METRICS



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