Hybrid Predictive Model on Detection of Neurodegenerative Disorder Using Machine Learning Classification Algorithms

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@article{1707314,
      author = {Oguoma Ikechukwu Stanley, Agbakwuru A.O, Amanze B.C},
      title = {Hybrid Predictive Model on Detection of Neurodegenerative Disorder Using Machine Learning Classification Algorithms},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {8},
      pages = {867-878},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707314.pdf},
      abstract = {The aim of this paper is to design a Hybrid Predictive Model on Detection of Neurodegenerative Disorder using Machine Learning Classification Algorithms, with major focus on detection of Alzheimer?s disease (AD), while the objectives is to ensure that the developed model can predict if a patients has Alzheimer disease or not through their hand writing on paper. The variables used for the predictions are collected amongst healthy people and Alzheimer patients which includes (Total_time, displacement, (gait movement rate time (gmrt_air reading), gmrt_paper reading, speed_air, speed_paper, num_of_pendown, pressure_mean) and a target class with (Patients = P and Healthy = H). The study employed three machine learning classification algorithm methods which include:  Support Vector Machine, Neural Network and Decision Tree algorithms. The data was analyzed with R and JASP platform while the experiments are done using DARWIN dataset containing 25 handwriting tasks with a total of 174 participants (89 Alzheimer patients and 85 healthy people) sourced from UCI and Kaggle machine learning repository. From the result, the experiment shows that the use of a hybrid approach involving three classification algorithms in health related data prediction to develop a model called (Ikem-Alzheimer-Model) is one of the best and more accurate method suitable for data prediction and hence has more percentage acceptance level when it comes to health issues, therefore it could be adopted for future use by medical practitioners to make decision on the subject matter.  Finally, the results prediction accuracy was concluded by comparing the three developed models involving their different F1 scores, confusion matrix, Evaluation Metrics, Roc Curves, and Precision (positive predictive value) shown in Table13 of this paper.},
      keywords = {Artificial Intelligence, Machine Learning, Health Science, Classification Model, Alzheimer disease prediction, health diagnosis and prediction of neurodegenerative disorder, Hybrid Predictive Model on neurodegenerative disorder.},
      month = {February},
  }