Comparative Study on Machine Learning models for Early Diagnose of Alzheimer's Disease: Multi Correlation Method

Kotturu, Praveen Kumar; Kumar, Abhishek

doi:10.1109/icces48766.2020.9137872

Cited by 19 publications

(1 citation statement)

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“…For health care problems machine learning algorithm are using for accurate prediction. In machine learning, model is train on the bases of past experience [12]. There are many machine learning algorithms such as random forest; Support vector; AdaBoost and KNN (K-Nearest Neighbor) etc.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Diagnosis of Alzheimer’s Disease using Comparative Study on Machine Learning Models

Zohra

Akram²,

Din

2021

VFAST trans. softw. eng.

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The method of diagnosing and treating diseases can be improved by identifying the genes that cause diseases. Alzheimer’s disease (AD) is one of the neurodegenerative disease that slowly destroys memory as well as thinking abilities. It’s important to diagnose Alzheimer’s disease (AD) early on so that adequate treatment can be given to patient. That article compares various machine learning models for identify Alzheimer’s Disease and proves that which algorithm gives the most reliable results in detecting AD in advance. Machine learning is a backbone of technology and everything in our life related to machine learning technologies. In this study various biomarkers are developed based on different machine learning classifiers like Random Forest, K-NN, Support Vector Machine, AdaBoost and XgBoost for AD gene detection. Genome data is extracted from NCBI related to Alzheimer disease. After that features are extracted from this genome data. Then above machine learning classifiers are train on these features. Different results are obtained by using Self-Consistency test and 10 Cross Validation test. Random Forest in both test gives 100% results. KNN gives 73.17% and 86.33%, SVM gives 100% and 97% AdaBoost gives 74.02% and 87.42%, XgBoost gives 86.04%and 92.56%accuracy for self-consistency and 10 Cross Validation test respectively.

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Section: Machine Learning Modelsmentioning

confidence: 99%