Developing a New MapReduce Framework with SVM-based Software Solution for Lyme Lesion Impact on Alzheimer's Disease Prevention

Alzheimer's disease has many symptoms to occur, one of the latest research studies identified that Lyme disease, caused by “Borrelia burgdorferi” also one of the reasons indirectly affect the neuroinflammation. The dataset used for this research study is supplied by many web sources, that are involved in feature extraction to apply on machine learning techniques for extracting meaningful knowledge. This work proposing a new MapReduce framework-based Support Vector Machine (SVM) model for better prediction of impact of Lyme on Alzheimer's disease. The MapReduce based framework using SVM model creates more impact on prediction of Alzheimer's disease at the earliest. The clinical data with images are collected from distributed environments and applied on preprocessing and feature extraction stages then applied to proposed MapReduce based SVM model for better accuracy and optimal classification for complex patters on Lyme image dataset. From these results the Alzheimer's risk factors are predicted for early treatment process. The results obtained by this study will give early decision making for highly available sources of treatment procedures and gives more insight on preventive care on each and every patient involved in diagnosis of Lyme disease prediction, lead to identify the Alzheimer's disease prediction.