Parkinson's disease is one of largest neurodegenerative disorder, touching over millions worldwide. The diagnosis of PD is complex because it requires careful clinical analysis of the patient's medical record. Unfortunately predicting the disease diagnosis is just low. Voice is one among the predictive and characteristic of PD, virtually each such patient experiences several vocal degradations (inability to provide sustained phonation, thermal and hoarseness) thus it is efficient to use voice data to diagnose the disease. Voice analysis offers the additional benefit of being non-invasive, low cost and simple to extract clinically. In this paper, we implemented various algorithms like Neural network, Random forest, SVM, XG Boost and KNN. Among all the algorithms used, Neural network turned up to be the best algorithm with an accuracy of 97.06%.
The most commonly used prediction technique is Ordinary Least Squares Regression (OLS Regression). It has been applied in many fields like statistics, finance, medicine, psychology and economics. Many people, specially Data Scientists using this technique know that it has not gone with enough training to apply it and should be checked why & when it can or can’t be applied.It’s not easy task to find or explain about why least square regression [1] is faced much criticism when trained and tried to apply it. In this paper, we mention firstly about fundamentals of linear regression and OLS regression along with that popularity of LS method, we present our analysis of difficulties & pitfalls that arise while OLS method is applied, finally some techniques for overcoming these problems.
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